Toward Practical Spectral Imaging beyond a Laboratory Context

Abstract

A portable, user-friendly multispectral imaging system assembled almost entirely of common photography equipment and open-source software has been developed. The system serves as an outreach and educational tool for demonstrating and promoting scientific imaging as a more routine practice in the contexts of cultural heritage digitization and photography. These efforts are aimed primarily at institutions where advanced imaging technologies are not already found, and where funding and expertise may limit access to commercial, bespoke multispectral imaging solutions that are currently available. The background and theory that were shared in tutorials given during the system’s initial testing campaign are detailed here. Testing was carried out in one-day on-site visits to six cooperating institutions of different sizes and collection types in the northeast USA. During these visits, the imaging system was presented, and the benefit of collecting spectral data using low barrier-to-entry capture and processing methods relative to conventional imaging methods was discussed. Imaging was conducted on site on selected collections objects to showcase the current capabilities of the system and to inform ongoing improvements to the setup and processing. This paper is a written companion piece to the visits, as a source of further detail and context for the two-light imaging system that was described and demonstrated.

Full text

Citation: Kuzio, O.R..; Farnand, S.P.
Toward Practical Spectral Imaging
beyond a Laboratory Context.
Heritage 2022,5, 4140–4160.
https://doi.org/10.3390/
heritage5040214
Academic Editors: Marcello Picollo
and Barbara Cattaneo
Received: 31 October 2022
Accepted: 7 December 2022
Published: 13 December 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
heritage
Article
Toward Practical Spectral Imaging beyond a Laboratory Context
Olivia R. Kuzio and Susan P. Farnand *
Munsell Color Science Laboratory, Program of Color Science, Rochester Institute of Technology, 1 Lomb Memorial
Drive, Rochester, NY 14623-5604 USA
*Correspondence: susan.farnand@rit.edu
Abstract:
A portable, user-friendly multispectral imaging system assembled almost entirely of
common photography equipment and open-source software has been developed. The system serves
as an outreach and educational tool for demonstrating and promoting scientific imaging as a more
routine practice in the contexts of cultural heritage digitization and photography. These efforts
are aimed primarily at institutions where advanced imaging technologies are not already found,
and where funding and expertise may limit access to commercial, bespoke multispectral imaging
solutions that are currently available. The background and theory that were shared in tutorials given
during the system’s initial testing campaign are detailed here. Testing was carried out in one-day
on-site visits to six cooperating institutions of different sizes and collection types in the northeast USA.
During these visits, the imaging system was presented, and the benefit of collecting spectral data
using low barrier-to-entry capture and processing methods relative to conventional imaging methods
was discussed. Imaging was conducted on site on selected collections objects to showcase the current
capabilities of the system and to inform ongoing improvements to the setup and processing. This
paper is a written companion piece to the visits, as a source of further detail and context for the
two-light imaging system that was described and demonstrated.
Keywords:
spectral imaging; multispectral imaging; color-accurate imaging; conservation documentation;
museum photography; cultural heritage digitization; spectral image archives
1. Introduction
1.1. Spectral Imaging in a Laboratory Context
Since its adoption by the heritage science community several decades ago [
1
], spectral
imaging has matured into an analytical technique regularly utilized in noninvasive scientific
studies of cultural heritage objects. Spectral imaging is differentiated from conventional
color imaging by the spectral resolution of the imaging system, where spectral imaging
techniques are those that collect more bands—as few as five or six, to hundreds—than
typical three-channel RGB imaging. Spectral imaging techniques may be further described
as multispectral or hyperspectral according to their relative spectral resolution (tens versus
hundreds of image bands), which is largely determined by the image band selection method
(e.g., filter- or illumination-based band selection versus diffraction grating wavelength
selection). The use of specific terms is fluid and often context-dependent [
2
]. Taken
together, spectral imaging techniques have been proven to be highly effective means of
performing noninvasive, spatially resolved reflectance spectroscopy in the UV-VIS-IR
range, which is particularly useful for identifying and mapping the distribution of artists’
materials [
3
,
4
]. This information is often used to build a better understanding of artists’
working methods, material degradation, and prior restoration campaigns, which together
provide an understanding of an object’s physical construction and history.
Despite their impressive abilities, due to the associated cost and complexity of these
techniques, spectral imaging capabilities are yet largely siloed in institutions with the
monetary and technical expertise necessary to support analytical imaging. Presently, mainly
specialized instruments are used to carry out spectral imaging. Many systems that have
Heritage 2022,5, 4140–4160. https://doi.org/10.3390/heritage5040214 https://www.mdpi.com/journal/heritage

Heritage 2022,54141
been developed for lab-based research or in situ field studies are experimental [
5
–
7
]. They
are typically applied in one-off technical studies, and are not intended or appropriate for
adoption within routine imaging workflows. Additionally, such systems are not necessarily
designed with user-friendly considerations, because they are built-to-purpose, and their
design and operation is aligned with the lab-based context of their development. They
more closely resemble analytical equipment rather than familiar cameras, and therefore
sacrifice intuitive handling properties in favor of technical utility. Finally, these complex
instruments are expensive to build, maintain, and operate, necessitating monetary support
that smaller institutions simply cannot fund.
1.2. Toward Spectral Imaging in a Studio Workflow
Observing these barriers to its widespread adoption by more institutions, an interest
developed in testing practical measures to put this technology into the hands of more users.
The priority was physically demonstrating the feasibility of adopting studio-practical
spectral imaging strategies during on-site visits to several institutions. The institutions
were of diverse size and geographic location, and each had different experience with,
approaches to, and goals for imaging collections. Dialogue with the smaller, more resource-
strapped institutions was of particular interest. Their feedback is most critical to determine
the best methods for getting this technology into the hands of more users moving forward.
More specifically, these visits were aimed at making both the concept and practice
of multispectral imaging more approachable for non-experts. It was introduced as an
advanced imaging technique, rather than a scientific analysis, and its advantages over
conventional color capture for more routine photography and digitization projects were
emphasized. These advantages, particularly for color-accurate reproduction [
1
,
8
,
9
], have
been recognized for some time and have been summarized previously [
10
,
11
]. They include:
• the elimination of the need for subjective visual editing in post-production,
•
the expansion of archives beyond a single set of viewing, illuminating, and ob-
server conditions (CIE illuminant D50 and 1931 standard observer for ICC color
managed archives),
•
the ability to re-render an image under any desired lighting condition to inform
curation, exhibition, scholarship, and conservation, and
•
the prevention of undesirable metameric matches of materials used in conserva-
tion treatments.
These advantages were conveyed during the course of the visits, which loosely con-
sisted of
•
a tutorial about the theory and practice of conventional color imaging versus the
proposed two-light spectral imaging method,
•
a demonstration of two-light imaging, showing how it can be carried out using mainly
cameras and equipment that are commonly found in photography studios and are
already familiar to cultural heritage imaging professionals, and
• discussion and questions around these activities.
These efforts to bring awareness and access to spectral imaging are the culmination
of recent research focused on developing more user friendly, affordable strategies for
spectral capture and processing [
12
–
17
]. They are based on a history of developments
for spectral imaging and color-accurate archiving of cultural heritage that have come out
of the RIT Munsell Color Science Lab’s Studio for Scientific Imaging and Archiving of
Cultural Heritage. The capture method of two-light imaging, to be introduced herein, is
an LED-based spin on the original filter-based Dual-RGB imaging technique [
18
,
19
]. With
respect to other spectral imaging techniques, dual-RGB and two-light imaging are notable
for their efficiency and low complexity. Imaging with either technique is fast, because
three spectral channels are collected per capture, and it is also accessible, because it can
be performed with familiar, commercially available DSLR and mirrorless cameras that are
already found in photography studios.

Heritage 2022,54142
The means of processing the spectral data collected via two-light imaging had to be
equally accessible and intuitive for the prospect of adopting this technique to be compelling.
Toward this end, custom image processing software was developed alongside two-light
imaging. The software, called Beyond RGB, is a cross-platform compatible, open source and
freely available stand-alone software application that is fully operable through an intuitive
graphical interface. It is designed to make processing the image sets as simple as possible.
A large portion of the on-site visits was dedicated to demonstrating the capabilities of the
first release of Beyond RGB and soliciting advice for improvements for future releases. Its
high-level functionality and role in the overall workflow will be described below, while a
more complete discussion can be found elsewhere [15].
1.3. Obstacles and Opportunities
The spectral image sets that are collected with two-light imaging are built up differently
than those collected using more traditional multispectral imaging processes, which capture
a single channel at a time sequentially over the wavelength range of interest. Conveying
the differences between these two strategies and clearly describing the theory and practice
of two-light imaging was a significant challenge. Furthermore, the most well-known
application of spectral imaging is deriving reflectance properties of materials and mapping
their distribution. In contrast, the emphasis in this work is on its color accuracy benefits.
Color calibration based on multispectral information is both a less-common and less-
intuitive use for spectral image data.
The details of two-light capture for highly color-accurate reproduction have been
discussed elsewhere [
16
], and will be further described in the Background section below.
Together with the Methods section that follows, these provide the information given in
the on-site tutorials: the benefits of two-light imaging with respect to conventional color
imaging, and an introduction to the setup, capture, and processing involved in the two-light
imaging workflow. The Results and Discussion section summarizes the general experience
of these real-world tests, impressions and feedback gathered from conversations with the
host institutions, and actionable suggestions that will be implemented to improve the utility
of the system moving forward.
2. Background
2.1. Dual-RGB Imaging
Given the time-consuming processes and expensive equipment necessary for analytical-
level spectral imaging, an alternative, less complex approach is desirable in the context of
studio photography. By the early 2000s, the Munsell Color Science Lab had been experimenting
with spectral-based color reproduction for a few years. After a number of studies and design
iterations to optimize filter choice and characteristics [
20
,
21
], the research culminated in the
introduction of a spectral imaging method in which a set of two optimized filters are paired
with a three-channel, RGB camera, enabling the capture of five spectral channels between two
filtered RGB images [
22
]. This spectral imaging strategy came to be called dual-RGB imaging,
and was later commercialized in the Sinar Color To Match (CTM) system [23].
At the time of its conception, the average color accuracy of dual-RGB of 0.9
∆
E
00
for
both calibration and verification data was superior to that of other contemporary, more
complex approaches [
22
]. These included a system utilizing a 31-band liquid-crystal tun-
able filter and monochrome sensor (average
∆
E
00
1.5) [
24
] and a 13-band system pairing
interference filters with a monochrome sensor (average
∆
E
00
1.5) [
25
]. This was an im-
portant result for the relevance of dual-RGB within studio photography, where obtaining
color-accurate images objectively and efficiently, thus overcoming the need for subjective,
time-consuming visual editing in post-processing, is an attractive selling point.
A high-level description of the capture and calibration strategies behind the dual-
RGB technique is given below. A more detailed account of the history of its development
and nuances in its implementation is available elsewhere [
19
], as well as image quality
considerations related to color transformations [26].

Heritage 2022,54143
Dual-RGB imaging is so named for the practice of capturing two images taken through
a blue-green filter and a yellow filter placed in front of the lens of an RGB color filter array
(CFA) camera. The spectral transmittance of such a pair of colored glass filters are plotted
in Figure 1. To increase throughput in the long red region of the visible spectrum above
~650 nm, improving spectral estimation accuracy in this region, the internal IR cut filter of
commercial RGB CFA cameras can be removed. A third, visible bandpass filter can then
also be used to more desirably tune the spectral transmission and still limit it to the visible
range (Figure 1, black line).
Figure 1.
Spectral transmittance of blue-green and yellow colored glass filters, and a visible bandpass filter.
Figure 2illustrates the result of pairing the blue-green and yellow filters with an IR-
modified RGB CFA camera. The spectral sensitivity of the red, green, and blue channels are
tuned differently by each colored filter, resulting in modified red, green, and blue channel
sensitivities that are different between the two captures, and can be combined to create a
six-channel spectral image stack.
Figure 2.
(
a
) Spectral sensitivity of a commercial RGB CFA camera that has had its IR filter removed.
(
b
) Spectral sensitivity of the camera when equipped with blue-green filter. (
c
) Spectral sensitivity of
the camera when equipped with a yellow filter.
The dual-RGB image stack can be calibrated for both colorimetric and spectral re-
flectance characterization via mathematical transformations that relate the six-channel

Heritage 2022,54144
spectral information (
Rbg Gbg Bbg RyGyBy
) of an imaged calibration target to its measured ref-
erence values. For colorimetric calibration, these reference values are the measured CIEXYZ
tristimulus values of the patches (
Xre f Yre f Zre f
), and for spectral reflectance calibration, they
are the measured spectral reflectance curves of the patches (Rλ1to Rλn).
Equation (1) shows the relationship between the dual-RGB camera signals and CIEXYZ
reference values. It is defined by the transformation matrix
MC
, which expanded is a 3-by-6
matrix, illustrated in Equation (2). The coefficients in the transformation matrix
MC
are
defined through iterative optimization, with the goal of minimizing the average CIEDE2000
color difference [
27
] between the dual-RGB camera signals averaged from a region inside
each patch of the calibration target and the target’s measured reference values.


Xre f
Yre f
Zre f

=Mcolor








Rbg
Gbg
Bbg
Ry
Gy
By








(1)
where
Mcolor =

m1,1 m1,2 m1,3 m1,4 m1,5 m1,6
m2,1 m2,2 m2,3 m2,4 m2,5 m2,6
m3,1 m3,2 m3,3 m3,4 m3,5 m3,6

(2)
After optimizing the transformation between six-channel camera signals and reference
target values, colorimetric calibration is complete. To obtain the color-calibrated image, the
transformation matrix is applied to the six-channel image stack, followed by the desired
linear color space matrix (e.g., ProPhotoRGB) and nonlinear encoding function. The process
of rendering a highly-color-accurate image from the dual-RGB image stack is complete.
The parallel process of spectral reflectance calibration based on the six-channel image
stack follows similar logic. The dual-RGB camera signals of the imaged calibration target
are related to measured spectral reflectance through the spectral reflectance transformation
matrix
MS
as shown in Equation (3).
R
is an n-by-1 vector, where n is determined by the
sampling of the reference spectral reflectance data (36 is typical: 380 nm to 730 nm in 10 nm
increments). It follows that
MS
has the dimensions n-by-6. Again, iterative optimization
is used to define the transformation matrix coefficients. In this case, the minimized value
is the root-mean-square error (RMSE) between the measured spectral reflectance and that
estimated from the average dual-RGB camera signals sampled from the calibration target.



Rλ1
.
.
.
Rλn



=MS








Rbg
Gbg
Bbg
Ry
Gy
By








(3)
where
MS=



m1,1 m1,2 m1,3 m1,4 m1,5 m1,6
.
.
..
.
..
.
..
.
..
.
..
.
.
mn,1 mn,2 mn,3 mn,4 mn,5 mn,6


(4)
Dual-RGB was developed to provide both accurate color reproduction and spectral
estimation, and importantly, it can easily be carried out using familiar cameras, studio light-
ing, inexpensive filters, and common color targets. As such, it served as the main influence
for developing two-light imaging, which similarly builds up a six-channel spectral image
cube, but does so through the use of tuned LED lighting, rather than filtered illumination.

Heritage 2022,54145
2.2. Two-Light Imaging
Tunable, multichannel LED light sources are now widely available and more afford-
able, and offer a number of advantages over filter-based wavelength selection. Forgoing the
need to screw on, slide, or otherwise shift filters into place reduces physical movement of
the system that leads to registration error between channels. Different filters may also have
varying optical properties that affect registration, and/or lead to large differences in the
optimal exposure between shots, which can affect image quality. For a small investment,
computer-controlled multichannel LED lights offer an elegant and flexible solution that can
be integrated into an automated capture routine. Furthermore, using narrowband LEDs
minimizes the object’s exposure to heat and other extraneous radiation [28,29].
Tunable light-based spectral imaging strategies that have been demonstrated and
are currently in use for cultural heritage imaging have mainly paired narrowband LEDs
with a monochrome sensor [
28
,
30
,
31
], echoing the common monochrome sensor + filter
multispectral imaging approach [
32
]. There have been some other recent studies in which a
three-channel RGB sensor is used [
33
,
34
]. This is the approach used here, in the two-light
imaging technique, in which a six-channel spectral stack is created from the combination
of two differently illuminated RGB captures. With the end goal of more approachable,
everyday access to this kind of advanced imaging, taking advantage of the inherent three-
channel nature of RGB CFA cameras provides an opportunity to explore utilizing familiar
professional-level cameras for more studio-friendly spectral imaging.
Early experiments in this research verified that a dual-RGB imaging can be carried
out effectively with a prosumer camera and inexpensive colored glass filters [
12
], and
then confirmed that only a two-fold increase in the number of image channels used for
colorimetric calibration, from the three channels of conventional RGB, to six spectral
channels, significantly improves color rendering accuracy [
13
]. Others have explored
trade-offs between filter-based versus light-based wavelength selection (i.e., dual-RGB
versus two-light) [14,16], and effects of camera and lens choice on color accuracy with the
two-light technique [17].
The two-light spectral imaging method was developed using ten-channel tunable
LED light sources. The spectral power distributions of the ten channels are plotted in
Figure 3a. A pair of lighting conditions, each consisting of a combination of three LEDs
from the overall set of ten, were created, and their spectral power distributions are plotted
in Figure 3b. The LEDs that make up each lighting condition were selected based on an
exhaustive search optimization, in which all the color accuracy of all possible pairs of
three-LED combinations was assessed, and the optimal combinations were computationally
identified for a given camera and calibration target. The details of this process have been
discussed elsewhere [
16
]. Note that the LED system currently utilized is a more advanced
equipment option than is necessary based on the results of the optimization process. Only a
subset of the ten channels is needed to create the two lighting conditions. These findings are
informing future research toward purpose-built, lower cost lighting for two-light imaging.
The capture and calibration of two-light spectral image stacks follows that of dual-RGB
imaging, where the objective is to modify the spectral sensitivity of the red, green, and blue
channels differently under each lighting condition, resulting in sensitivities that are shifted
relative to each other between the two captures, and can be combined to make a six-channel
spectral image stack. Figure 4illustrates the six channel sensitivity of the same IR modified
commercial camera as above in Figure 2, but paired with the two lighting conditions from
Figure 3b, rather than two filters.
Note that because the search optimization used for LED selection is defined around
the color rendering accuracy of a calibration target, the optimal lighting conditions depend
not only on the spectral characteristics of the camera sensitivity, but also on those of the
specific calibration target chosen. In other words, the optimal lighting conditions will differ
based on the camera and target used to define them. The RGB camera sensitivity shown in
Figure 4is that of an IR modified Sony
α
7R III, which has been used as the model prosumer

Heritage 2022,54146
camera throughout this research. The ideal lighting conditions plotted in Figure 3were
created for it using a Digital Color Checker SG as the calibrating target.
Figure 3.
(
a
) Spectral power distributions of the ten-channel LED lights, labeled by peak wavelength.
(
b
) The spectral power distributions of the pair of lighting conditions, each consisting of a mixture of
3 of the LEDs plotted in (
a
). For this camera and target, the optimal pairs are combinations of the
LEDs with peak wavelengths (1) 450 nm, 525 nm, and 735 nm, and (2) 450 nm, 545 nm, and 735 nm.
Figure 4.
(
a
) Spectral sensitivity of a commercial RGB CFA camera that has had its IR filter removed.
(
b
,
c
) Spectral sensitivity of the camera when imaging under lighting conditions 1 and 2 (shown in
Figure 3b).
The high-level capture and calibration strategies for carrying out two-light imaging
follow those of dual-RGB imaging described above, where colorimetric and spectral calibra-
tion are carried out on the six-channel spectral image stack that is the combination of RGB
image data collected under each of the optimized two lighting conditions. The colorimetric
and spectral reflectance transforms are determined using CIEDE2000- and RMSE-guided
matrix optimization over a reference color target. The final image can then be transformed,
encoded, and rendered to a high degree of color accuracy according to these calibrations.

Heritage 2022,54147
2.3. The Spectral Advantage
The CIEDE2000 matrix optimization method of colorimetric calibration, described
above, is a versatile and adaptable method of characterizing camera color with respect
to human perception [
35
]. In conventional imaging workflows, this process is what may
be more familiarly called color profiling. It can be extended to the calibration of imaging
systems with more than three channels, as was done here, with the definition of a larger
transformation matrix. The larger matrix includes coefficients that characterize the contribu-
tion of the signal captured in the additional channels in the estimation of the CIEXYZ values
for a given color in the image. While it may seem more intuitive to build up this transfor-
mation between quantities of the same dimensions, i.e., RGB camera signals to trichromatic
human vision, even in conventional three-channel imaging, a single camera channel does
not map one-to-one to a single tristimulus value—hence the need to define a 2D transfor-
mation matrix. The signals in each camera channel contribute in different amounts to the
estimation of each tristimulus value. The quantity of each channel’s contribution is defined
by the corresponding matrix coefficients. Increasing camera channels increases the degrees
of freedom in the transformation, which improves estimation accuracy.
It follows, then, that the six-channel two-light imaging color calibration outperforms
conventional RGB color calibration because of the increased amount of information used to
build the transformation matrix. The additional coefficients in the matrix provide a more
nuanced characterization of the visible spectrum, and operate as a means of better tuning
the estimated CIEXYZ values. This is illustrated in Figure 5, in which the color accuracy
of the Digital Color Checker SG target is simulated as rendered from conventional RGB
versus two-light imaging data captured with the same camera. The heat maps indicate the
color-coded
∆
E
00
color difference between the measured and rendered color of each patch
of the target. Upon visual inspection alone, the conventional RGB rendering contains more
lighter green- and yellow-coded patches, indicating that there are more patches rendered
with a larger color difference relative to the two-light rendering. This is summarized well
by comparing the mean and 90th percentile
∆
E
00
values across all the patches, reported
beneath each map. Those of the two-light method are far smaller; the 90th percentile value
of 0.4
∆
E
00
is especially notable, as it indicates that a large majority of the color differences
are below half of a
∆
E
00
unit, which is all but insignificant in the context of noticeable color
differences in digital images [36].
Finally, an extreme example illustrating shortcomings of conventional color imaging is
provided in Figure 6. A painting of the night sky that was imaged and rendered using both
conventional (Figure 6a) and two-light (Figure 6b) techniques exhibits large differences in
the appearance of a color found mainly along the horizon. To the eye, this paint looks blue,
and is not visually different from rest of the dark blue sky. This is confirmed by renderings
of the measured color of spots from both regions (Figure 6d). However, the measurements
reveal the presence of different blue pigments in the two regions, with that at the horizon
being cobalt blue, and the rest, phthalo blue. These two blue pigments, while visually
similar, have strikingly different spectral reflectance shapes (Figure 6c). The six-channel
sampling, particularly at longer visible wavelengths, leads to the large improvement of
rendering this color more accurately, reducing the large 16.5
∆
E
00
color difference between
the measured and the rendered color exhibited by conventional RGB imaging down to
3.0 ∆E00 (Figure 6d).

Heritage 2022,54148
Figure 5.
(
a
) Digital Color Checker SG. (
b
) A heat map that color codes the size of the
∆
E
00
color
difference between the measured color of each target patch and the color as rendered using conven-
tional RGB imaging. The mean and 90th percentile
∆
E
00
values across all of the patches are given
below the heat map. (c) The same as (b), but rendered using the two-light imaging technique.
Figure 6. Cont.

Heritage 2022,54149
Figure 6.
A painting of the night sky over a field imaged and rendered using conventional RGB
capture (
a
) and two-light capture (
b
). (
c
) Spectral reflectance measured from the two spots indicated
by the green circles in (
a
,
b
), which indicate the use of two different pigments, cobalt blue (spot 1)
and phthalo blue (spot 2) in painting the sky. (
d
) Comparison of the spot colors rendered from the
measured spectral reflectance versus from the RGB, and two-light image data, along with the
∆
E
00
color differences.
3. Capture and Image Processing Methods
3.1. Imaging System
3.1.1. Equipment
The main camera used throughout development, testing, and demonstration of two-
light imaging was the Sony
α
7R III, a 42MP mirrorless digital camera equipped with
pixel-shift multi-shot capabilities. This enables the direct capture of full-frame RGB images,
thus bypassing the need for computational demosaicing of the CFA pattern. The particular
camera used had its internal IR filter removed, evident in the plots of its spectral sensitivity
in Figures 2a and 4a, showing that the sensitivity of both the green and red channels extends
above 700 nm. The camera was always controlled via computer tether with Sony’s Imaging
Edge Remote.
The ten-channel tunable LED light sources paired with the camera were designed
by LEDMotive [
37
], and have the spectral radiance characteristics described and plotted
above (Figure 3a). The particular LEDs in the lights were previously chosen based on
research into the optimal ten-channel set for a cultural heritage imaging system following a
traditional sequential capture approach with a monochrome sensor [
31
]. Each light contains
the LEDs and an internal integrating sphere for diffusion of the light within a small housing
(16 ×12 ×12 cm).
Externally, parabolic reflectors are attached around the port to shape
the illumination and mimic studio strobe fixtures. The lights were also computer controlled
using MATLAB scripts that enabled independent control of each LED channel.
Because this research focused on demonstrating the color accuracy of the two-light
technique, color targets were a central part of the workflow for both calibration and
verification purposes. Those used most commonly included the Digital Color Checker
SG (X-Rite), the Next Generation Target V2 (Avian Rochester), and the Artist Paint Target
(Image Science Associates). The Digital Color Checker SG (CCSG) is a familiar target
that is already widely used in museum studio photography, and so it works well as an
accessible tool in this workflow (Figure 5a). The Next Generation Target (NGT) was
originally designed at the request of the Library of Congress to address concerns related
to durability, sensitivity to lighting geometry, as well as more appropriate color gamut
sampling for heritage materials [
38
] (Figure 7, left). Finally, the Artist Paint Target (APT),
which was originally developed in the Munsell Color Science Lab, is particularly useful as
a materially relevant target containing real artist paint mixtures [39] (Figure 7, right).

Heritage 2022,54150
Figure 7. Next Generation Target V2 (left) and Artist Paint Target (right).
3.1.2. Setup
Imaging was carried out in a copy stand configuration, using 45
°
/0
°
illumination/
detection geometry to mimic the bi-directional detection of the reference spectrophotometer
and reduce geometric error in the transfer between spectral reflectance measurement and
imaging. The typical image set collected included two-light image pairs of (1) the desired
color calibration target(s), (2) a flat field, (3) the dark current, with the lens cap covering the
lens, and (4) the object(s). The shutter speeds used for each of the two lighting conditions
were set by sampling the histograms of the calibration target’s white patches, and “exposing
to the right” (ETTR) at the base ISO, while avoiding clipping, in order to maximize use of
the sensor’s dynamic range [
40
]. All images were captured as photometric linear RAW files.
Because the only other physical components needed aside from the camera and lights
are a tripod and light stands to mount them on, and a laptop for tethered computer control,
the entire kit is easily packed into several Pelican cases for travel. This level of portability,
as well as the simplicity of the setup, was key in enabling its demonstration in a range of
different environments, including a small office, a classroom, a conservation lab, and a few
imaging studios. A photo of the imaging setup is included in Figure 8.
Figure 8. The two-light imaging setup in a classroom at the George Eastman Museum.

Heritage 2022,54151
3.2. Image Processing Software: Beyond RGB
There have been several software tools developed alongside the dual-RGB and other
multispectral imaging projects in the Munsell Color Science Lab for processing these
image data [
41
]. However, they found limited use outside the research lab environment
due to concerns with stability and ease of use. This was motivation to create a software
application to accompany two-light imaging that would be more easily adopted elsewhere,
and more intuitive to use. A team of senior software engineering undergraduates was
tasked with building such a solution, and the result was the development of Beyond RGB.
It is an application that facilitates implementation of the two-light technique by providing a
platform to process the resulting image data in a user-friendly way. Furthermore, it is cross-
platform compatible and locally installable on both Windows and macOS systems, and
it is open source and freely available at the project GitHub repository [
42
]. The form and
function of Beyond RGB are summarized below; further details can be found elsewhere [
15
].
Beyond RGB carries out the colorimetric and spectral calibrations on two-light spectral
image sets “under the hood” of a simple graphical user interface (Figure 9). It takes as input
the RAW image set, including flatfields, darks, and the images of the targets and object.
After the user provides information about the identity and spatial location of the color target,
pre-processing calibration proceeds automatically. Pre-processing involves flat-fielding
and dark current corrections to account for nonuniformities in the scene lighting and
sensor [
43
], and to remove the black level of the camera, as well as spatial registration of the
spectral channels to correct for chromatic aberration distortions between the two lighting
conditions. These operations are all completed prior to carrying out the colorimetric and
spectral calibration procedures outlined in the Background section above.
There is a simple image viewer built into Beyond RGB where the calibrated image is
populated when calibration is complete. The viewing window enables preliminary visual
inspection of the results, and includes a summary of the colorimetric data that characterizes
the accuracy of the calibration. Additionally, there is a built-in spectral picker that allows
the user to select regions of interest from which to display and export estimated reflectance
spectra based on the spectral reflectance transform (Figure 10). The main focus of the
application is encouraging the capture of spectral master files and enabling the calibration
and export of the color managed RGB image. However, the ability to perform spectral
estimation may be of more interest in future versions of the software that expand upon
the more familiar applications of spectral imaging, such as pigment characterization and
mapping. At present, the viewer and spectral picker are most useful for identifying regions
of interest that may be of interest for further study by complementary techniques.

Heritage 2022,54152
Figure 9.
A screenshot of the Beyond RGB graphical user interface illustrating interactive target
patch selection.
Figure 10.
A screenshot showing the spectral picker built into the Beyond RGB calibrated im-
age viewer.
4. Results and Discussion
4.1. Institutions Visited
This final section summarizes the general successes and lessons learned over the
course of the demonstration and testing visits. The institutions visited differed in size,
kinds of collections, and geographic location, and included
• the Cary Graphic Arts Collection at the RIT Libraries (Rochester, NY, USA),
• the National Cryptologic Museum (Annapolis Junction, MD, USA),

Heritage 2022,54153
• the US Army Heritage and Education Center (Carlisle, PA, USA),
• the Museum of Modern Art (New York, NY, USA),
• the Art Conservation Department at Buffalo State College (Buffalo, NY, USA), and
• the George Eastman Museum (Rochester, NY, USA)
A majority of the visits included both an introductory presentation on the technique
and its advantages over conventional color imaging, followed by a demonstration of the
system while imaging some collections objects.
4.2. Example Results
The series of images below (Figures 11–14) are representative selections from those
captured of collections objects during the visits. They were chosen to show examples of the
range of materials and colors imaged. They include the marbled paper inside cover and the
title page of an embroidered book from the Cary Graphic Arts Collection, a painted flag and
a felt uniform patch, both from the US Army Heritage and Education Center collection, and
a platinotype photograph from a study collection at RIT that was included in a previous art
reproduction case study [
44
]. All images were calibrated using the Digital Color Checker
SG. The flag, patch, and photograph were captured with both the conventional color
imaging and two-light imaging to enable qualitative comparison between the two. For the
book, only the two-light image rendering is available. It is included as an example of the
diverse material that was imaged. Additionally, spot measurements made with a handheld
spectrophotometer were collected on a few regions of interest on each object to provide
ground truth against which the rendered colors are compared quantitatively. For each
spot, the measured, conventional color imaging (where available), and two-light imaging
CIELAB values are reported, as well as the corresponding
∆
E
00
color difference between
the measured and rendered colors.
Figure 11.
An image of the marbled paper inside cover and first page of an embroidered book
from the Cary Graphic Arts Collection rendered from a two-light spectral capture. A handheld
spectrophotometer was used to measure the color at the three locations marked on the image. The
CIELAB values from the measurement and the image at each location, and the
∆
E
00
between them,
are reported in the table.

Heritage 2022,54154
Figure 12.
“Flag Mid-20th Century”, 1936–1965, U.S. Army Heritage and Education Center, Carlisle,
PA. An image of a flag painted with the insignia of the Chairman of the Joint Chiefs of Staff rendered
from a conventional color capture (
left
) and a two-light spectral capture (
right
). A handheld spec-
trophotometer was used to measure the color at the three locations marked on the conventional color
rendering. The CIELAB values from the measurement and both images at each location, and the
∆
E
00
between them, are reported in the table.
Figure 13.
“Insignia Mid-20th Century”, 1936–1965, U.S. Army Heritage and Education Center,
Carlisle, PA. An image of a wool and felt 1st Infantry Division uniform patch rendered from a con-
ventional color capture (
left
) and a two-light spectral capture (
right
). A handheld spectrophotometer
was used to measure the color at the three locations marked on the conventional color rendering. The
CIELAB values from the measurement and both images at each location, and the
∆
E
00
between them,
are reported in the table.

Heritage 2022,54155
Figure 14.
An image of a historic platinotype photograph from a study collection at RIT rendered
from a conventional color capture (
left
) and a two-light spectral capture (
right
). A handheld spec-
trophotometer was used to measure the color at the three locations marked on the conventional color
rendering. The CIELAB values from the measurement and both images at each location, and the
∆
E
00
between them, are reported in the table.
Unsurprisingly, two-light imaging outperformed conventional color imaging across
the board, showing smaller color differences with respect to the true color in the sampled
regions. The largest of these color differences are visually obvious when comparing the
less accurate to the more accurate rendering. The images of the flag were included as an
example of one of these most noticeably different renderings, in the blue background and
stars. The Red 1 patch is included because the conservators commented that the olive green
color is one that they have noticed in particular that does not photograph well. Two-light
imaging appears to have reduced this problem. The platinotype photograph is included
because it proved to be a particularly difficult object to reproduce well by museum imaging
methods that were in place in a study carried out a decade ago [
44
]. The photograph
has a very flat reflectance curve across the visible range that is not well characterized
by three-channel sampling, but two-light imaging better samples this curve shape, and
produces comparatively less of the pinkish cast evident in the conventional color rendering.
It is worth noting again that all of these images were calibrated using the Digital Color
Checker SG. For the two-light images, the color-calibrated mean
∆
E
00
across the target
did not exceed 1
∆
E
00
. A typical
∆
E
00
target heat map result for one of the calibrations
is provided in Figure 15, in which the mean
∆
E
00
is 0.8. However, the mean level of
color accuracy of 0.8
∆
E
00
is not achieved in the regions of interest checked with spot
measurements. This is a somewhat expected result when using a commercial target that is
not the same material nor a close representation of the gamut of colors in the real objects.
This shows the value that custom, materially specific and color-curated targets can add to
a workflow.
As an aside, the cause of the single outlier value of 5.0
∆
E
00
for patch C6 in Figure 15
is unknown. There is a possibility that this particular patch of the semi-glossy target
caught a glare during capture, throwing off its calibration. The presence of outliers like
these illustrate the value in reporting the mean and 90th percentile
∆
E
00
values as more

Heritage 2022,54156
representative statistics describing the reproduction of the vast majority of the patches in
the set.
Figure 15.
An example of a green-to-red color-coded heat map visualization (
left
) of the magnitude
of the
∆
E
00
color difference between the reference data and rendered image data for the CCSG (
right
)
that was a typical result for a two-light spectral capture from the on-site visits.
4.3. Feedback and Future Work
The demonstrated adherence to copy stand lighting geometry was a common source
of concern. This setup provides consistency with 45
°
/0
°
spectrophotometric target mea-
surement geometry. While it results in technical accuracy, this approach does not leave
room for using more complicated illumination setups that better highlight the character of,
for example, a painting’s surface texture, nor does it reproduce what one might expect an
artwork to look like under gallery lighting conditions. A previous case study found that for
conventional color imaging, as long as exposure is set correctly, straying from copy stand
lighting geometry had little effect on final color accuracy [
45
]. This remains to be verified
for two-light imaging, but opens the door to possibilities for more creative lighting setups,
or even integration with multi-light 2.5D imaging techniques for simultaneously recording
color and surface texture [46].
Among the most valuable discussions were those related to improvements and ad-
ditional features and functions in Beyond RGB. This was a particularly important aspect
to gather feedback on, as the project timeline for the development and release of the first
version of the software did not allow much time for user testing of the software ahead of
the visits. Positive impressions of the software included the simplicity of interacting with
it through a graphical user interface, the rendered image viewer and spectral picker as
a first-pass inspection tool, and the open-source, free-to-use nature of the project. There
were many helpful comments made about small ways to refine the graphical user interface
to improve functionality and flow, like drag-and-drop file import, auto-populating fields
based on expected naming conventions, and automated target detection. Some of the most
important, big picture suggestions for improvements included:
•
Batch processing. At present, a single calibration run of the software handles a
single object image at a time, requiring time-consuming resetting of the calibration
parameters for each run. Batch processing would enable the user to calibrate an entire
set of images captured under the same calibration conditions much more efficiently.
•
A project website. Currently, the project is hosted entirely on its GitHub repository.
This can be difficult to navigate for novice users who only need access to the installation
packages, Wiki, and user guide. These distributables would be better housed on a
separate, dedicated website.

Heritage 2022,54157
•
An Adobe Camera Raw (ACR) RAW to TIFF workflow. The current version of Beyond
RGB was tested with and supports Canon, Fujifilm, Nikon, and Sony RAW file formats.
For cases where input images are not in one of these formats, it also supports uncom-
pressed, unprocessed linear TIFFs created from RAW files. As ACR is a popular tool
for working with RAW images, it was requested that specific guidelines for setting the
correct parameters to obtain unprocessed TIFFs from RAWs using ACR be provided.
•
Material mapping. The ability to estimate pixel-by-pixel reflectance spectra is a feature
that already exists in Beyond RGB (Figure 10). Building in the capability to group and
visualize the distribution of reflectance spectra having similar spectral features would
be the first steps toward exploring the applicability of two-light imaging to the more
typical tasks of spectral imaging, like pigment characterization and mapping.
These suggestions and many more, both big and small, were compiled into a master
wish list of features that are currently at the center of ongoing updates to Beyond RGB.
They will be available in the next version of the software and documentation, which is
anticipated in Spring 2023.
The feedback and impressions from the visits were overall positive. The needs and
priorities for imaging at each institution differ, and conveying the benefits that two-light
imaging could offer in these varied contexts was successful. The fact that two-light imaging
involves a specific set of capture and processing procedures is useful in and of itself. For
instance, this could aid in providing a more structured approach to documentation imaging
in conservation labs in which current practice is less rigorously controlled and outdated
cameras are used. Similarly, in institutions without dedicated imaging personnel or space,
this is an efficient, portable means of capturing accurate color that largely removes time-
consuming, subjective post-production corrections. Two-light imaging is also a useful
teaching tool for introducing not just the concepts and practice of spectral imaging, but also
human vision, color appearance, and quality control at academic and teaching institutions.
The system that was demonstrated is a proof-of-concept prototype. Presently, it still
utilizes LED light sources that are likely out-of-budget for all but larger institutions. These
visits were critical to emphasize to the community that with growing interest, there is
the potential for future development of simplified lighting solutions that would be a less
costly option for carrying out two-light imaging. Alternatively, tunable broadband LED
lamps, such as the Broncolor F160, are now becoming a more common studio lighting
option. It is not outside the realm of possibility to imagine that a dual-purpose fixture,
integrating narrowband channels into such an existing LED system, to meet the needs
of both conventional and two-light imaging, could be an attractive option. Regardless,
the interest expressed in pursuing such ideas was encouraging, and shows that there is
momentum for continuing to improve access to these advanced imaging practices across
more institutions.
5. Conclusions
The two-light imaging technique has been demonstrated as an effective capture
method for color-accurate rendering and spectral archiving that is practical for integration
with routine studio photography workflows. The descriptions of the influential history and
development of dual-RGB imaging and the method of transforming six-channel spectral
data to a calibrated color-accurate rendering provide context for two-light imaging, and
further supplement the information from the on-site demonstrations. Traveling with the
system to several institutions with varied imaging capabilities and goals was the first true
test of its flexibility beyond the research and development environment. It also provided
the opportunity to describe and demonstrate the advantages of the two-light imaging tech-
nique to diverse audiences, and to gather impressions about how the system might fit into
existing imaging workflows. This feedback is informing updates and improvements to the
system moving forward, which will focus especially on adding features to the Beyond RGB
software and developing lower-cost lighting solutions optimized specifically for two-light
imaging. These will also influence continued efforts to collaborate with institutions to

Heritage 2022,54158
provide accessible education around spectral imaging, and to communicate the ways it
might enhance current frameworks of cultural heritage digitization and archiving.
Author Contributions:
Conceptualization, O.R.K. and S.P.F.; methodology, O.R.K. and S.P.F.; soft-
ware, O.R.K.; validation, O.R.K.; formal analysis, O.R.K.; investigation, O.R.K.; resources, S.P.F.; data
curation, O.R.K.; writing—original draft preparation, O.R.K.; writing—review and editing, O.R.K.
and S.P.F.; visualization, O.R.K.; supervision, S.P.F.; project administration, S.P.F.; funding acquisition,
S.P.F. All authors have read and agreed to the published version of the manuscript.
Funding:
Funding for this research was provided by the Max Saltzmann Endowed Fellowship in the
Color Science of Cultural Heritage at RIT.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors thank their generous hosts at each of the institutions visited for
demonstration and testing: Steve Galbraith, RIT Cary Graphic Arts Collection (Rochester, NY, USA);
Rob Simpson, National Cryptologic Museum (Annapolis Junction, MD, USA); Jordan Ferraro, Cynthia
Blechl, and Geoffrey Manglesdorf, US Army Heritage and Education Center (Carlisle, PA, USA);
Robert Kastler, Denis Doorley, and Emile Askey, Museum of Modern Art (New York, NY, USA); Jiuan
Jiuan Chen and Patrick Ravines, SUNY Buffalo Department of Art Conservation (Buffalo, NY, USA);
and Elizabeth Chiang, George Eastman Museum (Rochester, NY, USA). Additional thanks to Yosi
Pozeilov, Los Angeles County Museum of Art (Los Angeles, CA, USA), for testing and providing
valuable feedback about using and improving Beyond RGB.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Saunders, D.; Cupitt, J. Image Processing at the National Gallery: The VASARI Project. Natl. Gallery Tech. Bull. 1993,14, 72–85.
2.
Striova, J.; Dal Fovo, A.; Fontana, R. Reflectance imaging spectroscopy in heritage science. Riv. Del Nuovo Cim.
2020
,43, 515–566.
[CrossRef]
3.
Jung, A. Hyperspectral Imaging. In Digital Techniques for Documenting and Preserving Cultural Heritage; Bentkowska-Kafel, A.,
MacDonald, L., Eds.; Arc Humanities Press: Leeds, UK, 2018; pp. 217–219.
4.
Delaney, J.K.; Dooley, K.A.; van Loon, A.; Vandivere, A. Mapping the pigment distribution of Vermeer’s Girl with a Pearl Earring.
Herit. Sci. 2020,8, 1–16. [CrossRef]
5.
Liang, H.; Lucian, A.; Lange, R.; Cheung, C.; Su, B. Remote spectral imaging with simultaneous extraction of 3D topography for
historical wall paintings. ISPRS J. Photogramm. Remote Sens. 2014,95, 13–22. [CrossRef]
6.
Cucci, C.; Delaney, J.K.; Picollo, M. Reflectance Hyperspectral Imaging for Investigation of Works of Art: Old Master Paintings
and Illuminated Manuscripts. Accounts Chem. Res. 2016,49, 2070–2079. [CrossRef] [PubMed]
7.
George, S.; Hardeberg, J.Y.; Linhares, J.; Macdonald, L.; Montagner, C.; Nascimento, S.; Picollo, M.; Pillay, R.; Vitorino, T.; Webb,
E.K. A Study of Spectral Imaging Acquisition and Processing for Cultural Heritage. In Digital Techniques for Documenting and
Preserving Cultural Heritage; Bentkowska-Kafel, A., MacDonald, L., Eds.; Arc Humanities Press: Leeds, UK, 2018; Chapter 8,
pp. 141–158. [CrossRef]
8.
Martinez, K.; Cupitt, J.; Saunders, D.R. High-resolution colorimetric imaging of paintings. In Proceedings of the Cameras,
Scanners, and Image Acquisition Systems Conference, San Jose, CA, USA, 31 January–5 February 1993; Volume 1901, pp. 25–36.
[CrossRef]
9.
Ribés, A.; Schmitt, F.; Pillay, R.; Lahanier, C. Calibration and spectral reconstruction for CRISATEL: An art painting multispectral
acquisition system. J. Imaging Sci. Technol. 2005,49, 563–573.
10.
Berns, R.S. Color-Accurate Image Archives Using Spectral Imaging. In Scientific Examination of Art: Modern Techniques in
Conservation and Analysis; The National Academies Press: Washington, DC, USA, 2005; Chapter 8, pp. 105–119.
11.
Wyble, D.R. Spectral Imaging: A Non-technical Introduction to What It Is, and Why You Should Care. In Proceedings of the
Archiving 2021 Conference (Short Course Notes), Online, 8–24 June 2021.
12.
Kuzio, O.R.; Berns, R.S. Color and Material Appearance Imaging and Archiving Using a Sony Alpha a7R III Camera; Technical Report’;
Rochester Institute of Technology: Rochester, NY, USA, 2018.
13.
Kuzio, O.; Farnand, S. Color Accuracy-Guided Data Reduction for Practical LED-based Multispectral Imaging. In Proceedings of
the Archiving 2021 Conference, Online, 8–24 June 2021; pp. 65–70. [CrossRef]
14.
Kuzio, O.; Farnand, S. LED-based versus Filter-based Multispectral Imaging Methods for Museum Studio Photography. In
Proceedings of the International Colour Association Conference 2021, Online, 30 August–3 September, 2021; pp. 639–644.
15.
Kuzio, O.R.; Farnand, S.P. Beyond RGB: A spectral image processing software application for cultural heritage studio photography.
In Proceedings of the Archiving 2022 Conference, Online, 7–10 June 2022; pp. 95–100. [CrossRef]

Heritage 2022,54159
16.
Kuzio, O.R.; Farnand, S.P. Comparing Practical Spectral Imaging Methods for Cultural Heritage Studio Photography. J. Comput.
Cult. Herit. 2022, just accepted. [CrossRef]
17.
Kuzio, O.; Farnand, S. Simulating the Effect of Camera and Lens Choice for Color Accurate Spectral Imaging of Cultural Heritage
Materials. In Proceedings of the International Colour Association (AIC) Conference 2022, Online, 13–16 June 2022; p. TBD.
18.
Chen, T.; Berns, R.S. Measuring the Total Appearance of Paintings Using a Linear Source, Studio Strobes, and a Dual-RGB Camera;
Technical Report; Rochester Institute of Technology: Rochester, NY, USA, 2012.
19.
Berns, R.S. Theory and Practice of Dual-RGB Imaging; Technical Report; Rochester Institute of Technology: Rochester, NY, USA,
2016.
20.
Imai, F.H.; Taplin, L.A.; Day, E.A. Comparative Study of Spectral Reflectance Estimation Based on Broad-Band Imaging Systems; Technical
Report; Rochester Institute of Technology: Rochester, NY, USA, 2003.
21.
Berns, R.S.; Taplin, L.A.; Nezamabadi, M.; Zhao, Y. Modifications of a Sinarback 54 Digital Camera for Spectral and High-Accuracy
Colorimetric Imaging: Simulations and Experiments; Technical Report; Rochester Institute of Technology: Rochester, NY, USA, 2004.
22.
Berns, R.S.; Taplin, L.A.; Nezamabadi, M.; Mohammadi, M.; Zhao, Y. Spectral imaging using a commercial colour-filter array
digital camera. In Proceedings of the Fourteenth Triennial ICOM-CC Meeting, The Hague, The Netherlands, 12–16 September
2005; pp. 743–750.
23. Sinar. Color To Match. Available online: https://sinar.swiss/products/cameras/ctm/ (accessed on 8 October 2022).
24.
Berns, R.S.; Taplin, L.A.; Imai, Francisco, H.; Day, E.A.; Day, D.C. A Comparison of Small-Aperture and Image-Based Spectropho-
tometry of Paintings. Stud. Conserv. 2005,50, 253–266. [CrossRef]
25.
Liang, H.; Saunders, D.; Cupitt, J. A New Multispectral Imaging System for Examining Paintings. J. Imaging Sci. Technol.
2005
,
49, 551–562.
26.
Berns, R.S. Image Quality Degradation Caused by Color Transformations in Multispectral Imaging—A Practical Review. In
Proceedings of the Archiving 2020 Conference, Online, 18–21 May 2020; pp. 60–68. [CrossRef]
27.
Sharma, G.; Wu, W.; Dalal, E.N. The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and
mathematical observations. Color Res. Appl. 2005,30, 21–30. [CrossRef]
28.
Christens-Barry, W.A.; Boydston, K.; France, F.G.; Knox, K.T.; Easton, R.L., Jr.; Toth, M.B. Camera system for multispectral
imaging of documents. In Proceedings of the Sensors, Cameras, and Systems for Industrial/Scientific Applications Conference X,
San Jose, CA, USA, 20–22 January 2009; Bodegom, E., Nguyen, V., Eds.; SPIE: 2009; Bellingham, WA, USA. Volume 7249, p. 724908.
[CrossRef]
29.
France, F.G.; Toth, M.B.; Christens-Barry, W.A.; Boydston, K. Advanced Spectral Imaging for Microanalysis of Cultural Heritage.
Microsc. Microanal. 2010,16, 728–729. [CrossRef]
30.
Gibson, A.; Piquette, K.E.; Bergmann, U.; Christens-Barry, W.; Davis, G.; Endrizzi, M.; Fan, S.; Farsiu, S.; Fitzgerald, A.; Griffiths,
J.; et al. An assessment of multimodal imaging of subsurface text in mummy cartonnage using surrogate papyrus phantoms.
Herit. Sci. 2018,6, 7. [CrossRef]
31.
Paray, J.N. LED Selection for Spectral (Multispectral) Imaging. Master’s Thesis, Rochester Institute of Technology, Rochester, NY,
USA, 2020.
32.
Berns, R.S. Digital color reconstructions of cultural heritage using color-managed imaging and small-aperture spectrophotometry.
Color Res. Appl. 2019,44, 531–546. [CrossRef]
33.
Shrestha, R.; Hardeberg, J.Y. An experimental study of fast multispectral imaging using LED illumination and an RGB camera. In
Proceedings of the 23rd Color and Imaging Conference, Darmstadt, Germany, 19–23 December 2015; pp. 36–40.
34.
Shrestha, R.; Hardeberg, J.Y. Assessment of Two Fast Multispectral Systems for Imaging of a Cultural Heritage Artifact—A
Russian Icon. In Proceedings of the 14th International Conference on Signal Image Technology and Internet-Based Systems, Las
Palmas de Gran Canaria, Spain, 26–29 November 2018; pp. 645–650. [CrossRef]
35.
Fairchild, M.D.; Wyble, D.R.; Johnson, G.M. Matching image color from different cameras. In Proceedings of the Image Quality
and System Performance Conference V, San Jose, CA, USA, 28–30 January 2008; Volume 6808. [CrossRef]
36.
Stokes, M.; Fairchild, M.D.; Berns, R.S. Precision Requirements for Digital Color Reproduction. ACM Trans. Graph.
1992
,
11, 406–422. [CrossRef]
37.
Ledmotive Technologies. SPECTRA TUNE LAB: The Light Engine for Scientists. Available online: https://ledmotive.com/stlab/
(accessed on 23 February 2021).
38.
Wyble, D.R. Next generation camera calibration target for archiving. In Proceedings of the Archiving 2017 Conference, Riga,
Latvia, 15–18 May 2017; pp. 127–132. [CrossRef]
39.
Berns, R.S. Artist Paint Target (APT): A Tool for Verifying Camera Performance; Technical Report; Rochester Institute of Technology:
Rochester, NY, USA, 2014.
40.
Reichmann, M. Expose Right. 2003. Available online: https://luminous-landscape.com/expose-right/ (accessed on 8 October
2022).
41.
Studio for Scientific Imaging and Archiving of Cultural Heritage: Software. Available online: https://www.rit.edu/science/
studio-scientific-imaging-and-archiving-cultural-heritage#software (accessed on 8 October 2022).
42.
Beyond RGB: Initial Releases. 2022. Available online: https://github.com/BeyondRGB/Imaging-Art-beyond-RGB/releases
(accessed on 8 October 2022).

Heritage 2022,54160
43.
Witwer, J.; Berns, R.S. Increasing the versatility of digitizations through post-camera flat-fielding. In Proceedings of the Archiving
2015 Conference, Los Angeles, CA, USA, 19–22 May 2015; pp. 110–113.
44. Frey, F.S.; Farnand, S. Benchmarking Art Image Interchange Cycles; RIT School of Print Media: Rochester, NY, USA, 2011.
45.
Geffert, W.S. Transitioning to international imaging standards at the Metropolitan Museum of Art’s Photograph Studio: A case
study. In Proceedings of the Archiving 2011 Conference, Salt Lake City, UT, USA, 16–19 May 2011; pp. 205–210.
46.
Cox, B.D.; Berns, R.S. Imaging artwork in a studio environment for computer graphics rendering. In Proceedings of the
SPIE/IS&T Electronic Imaging Conference, San Francisco, CA, USA, 8–12 February 2015. [CrossRef]