A Review on Image Enhancement Techniques

Abstract

Image Enhancement is one of the most important and complex techniques in image processing technology. The main aim of image enhancement is to improve the visual appearance on an image and to offer a better representation of the image for Computer Vision Algorithms. In this paper,we is covered a few application fields of image enhancement with various images like grayscale, color, infrared and even with videos. The main objective of this paper is to highlight the drawbacks of the state of the art image enhancement techniques.

Full text

Southeast Europe Journal of Soft Computing
Available online:
VOL.8 NO.1March 2019 -
ISSN 2233
A Review
on
Haris Ačkar
,
Ali Abd Almisreb
Article Info
Article history:
Article received on 13 January 2018
Received in revised form 25
February2018
Keywords:
Enhancement;underwater images,
medical images, deffoging
algorithms, infrared images,
heuristic methods.
1. INTRODUCTION
Image enhancement is one of the preprocessing steps in
various computer vision applications. It is the process of
improving the quality of the image without any info
loss. For example in medical images [1], contrast
enhancement is mostly used for increasing the quality of
any image. The basic importance of the contrast
enhancement is to improve the pixel brightness in medical
application. A lot of methods are i
ntroduced in research
papers, and every method has one or few application fields.
Depend on the field application, types of images, grayscale
or color images, we need to choose the proper method for
Faculty of Engineering and Natural Sciences,
International University of Sarajevo,
Hrasnicka c
esta 15, Ilidža 71210
Bosnia and Herzegovina
haris_ackar@hotmail.com
Southeast Europe Journal of Soft Computing
Available online:
http://scjournal.ius.edu.ba
ISSN 2233
– 1859
on
Image Enhancement Techniques
Ali Abd Almisreb
Mohamed A. Saleh
Abstract
Image Enhancement is one of the most important and complex techniques
in image processing technology. The main aim of image enhancement is
to improve the visual appearance on an image and to offer a better
representation of the image for Computer
Vision Algorithms. In this
paper,we
is covered a few application fields of image enhancement with
various images like grayscale, color, infrared and even with videos. The
main objective of this paper is to highlight the drawbacks of the state of
the art image enhancement techniques.
Image enhancement is one of the preprocessing steps in
various computer vision applications. It is the process of
improving the quality of the image without any info
rmation
loss. For example in medical images [1], contrast
enhancement is mostly used for increasing the quality of
any image. The basic importance of the contrast
enhancement is to improve the pixel brightness in medical
ntroduced in research
papers, and every method has one or few application fields.
Depend on the field application, types of images, grayscale
or color images, we need to choose the proper method for
image enhancement. In this paper will be presented state
the art techniques for image enhancement of grayscale and
color images. A short description of those methods will be
provided and their field of application. Every Computer
Vision algorithm requires high-
visibility input images in
order to produce some
output. However, in most situations,
images are taken in some specific conditions like low
Therefore, we need to enhance those images before further
processing in order to have correct results from Computer
Vision algorithms. In general, image enhan
need to „correct“ input images for specific purpose so that
those images can be more suitable for specific Computer
Vision Algorithms.
Faculty of Engineering and Natural Sciences,
International University of Sarajevo,
esta 15, Ilidža 71210
Sarajevo,
Bosnia and Herzegovina
Faculty of Electrical Engineering,
Universiti Teknologi MARA (UiTM),
Malaysia
Image Enhancement is one of the most important and complex techniques
in image processing technology. The main aim of image enhancement is
to improve the visual appearance on an image and to offer a better
Vision Algorithms. In this
is covered a few application fields of image enhancement with
various images like grayscale, color, infrared and even with videos. The
main objective of this paper is to highlight the drawbacks of the state of
image enhancement. In this paper will be presented state
of
the art techniques for image enhancement of grayscale and
color images. A short description of those methods will be
provided and their field of application. Every Computer
visibility input images in
output. However, in most situations,
images are taken in some specific conditions like low
-light.
Therefore, we need to enhance those images before further
processing in order to have correct results from Computer
Vision algorithms. In general, image enhan
cement methods
need to „correct“ input images for specific purpose so that
those images can be more suitable for specific Computer
Faculty of Electrical Engineering,
Universiti Teknologi MARA (UiTM),

43
H. Ackar et al./ Southeast Europe Journal of Soft Computing Vol.8 No.1 March 2019 (42-48)
This paper is organized into 8 sections, Section 1 gives
an overview of the paper. It describes the importance of
image enhancement in image processing. Section 2 gives a
literature review for image enhancement techniques for
underwater images. Section 3 gives a literature review for
image enhancement techniques for medical images. Section
4 gives a short review of video and image defogging
algorithms. Section 5 gives an overview of techniques for
infrared image enhancement and sections 6 gives a
literature review for contrast enhancement. Section 7 gives
a short review of Haze visibility enhancement of images.
Section 8 gives a short review of latest image enhancement
techniques from 2018 and 2019.
2. IMAGE ENHANCEMENT TECHNIQUES
FOR UNDERWATER IMAGES
Underwater images are corrupted due to scatters and
amalgamation, resulting in low contrast and color distortion
[2]. There are many image enhancement techniques such as
white balance, color correction, histogram equalization, and
fusion-based methods [3]. Various image enhancement
techniques can be found in the literature. Hue variations can
be minimized by wavelength compensation [4]. Also,
underwater images have low perceivability, small
divergence and lessening hues [5]. To enhance underwater
images, some authors propose unsupervised digital image
color equalization [6]. Other authors use histogram
modifications techniques to increase the quality of
underwater image [7, 8], and there are also researchers who
purposes algorithms to reduce underwater perturbations and
to improves image quality [9, 10].
3. IMAGE ENHANCEMENT TECHNIQUES
FOR MEDICAL IMAGES
In the medical images, the main role plays contrast
enhancement to increase the quality of an image [11]. Edge
detection is also playing an important role in medical
imaging because all information has preserved in edges. In
literature can be found a lot of interesting image
enhancement techniques for medical devices, classic but
also and fuzzy approaches. Some researchers propose fuzzy
hyperbolization to increase the clarity of the image [12].
There are also techniques for medical images using type-II
fuzzy set [13]. The detailed description of type-II fuzzy set
can be found in [14]. Survey paper like [15] presenting a
detailed overview of image enhancement using fuzzy
techniques. The main advantage of those techniques is to
enhance the contrast and improve the quality of the image.
A lot of authors are proposing various medical image
enhancement techniques using type-II fuzzy approach. The
[15] proposes type-II fuzzy computing techniques for
image enhancement. The [16] proposes Edge detection
method based on type-II fuzzy system for color images. At
the end, papers like [17] can be found in literature, those
papers describe the development of improved fuzzy rule-
based edge detection technique.
4. VIDEO AND IMAGE DEFOGGING
ALGORITHMS
Videos and images acquired by a visual system are
seriously degraded under hazy and foggy weather, which
will affect the detection, tracking, and recognition of
targets. In that purpose a lot of video and image defogging
algorithms is created. The images acquired by the camera
in foggy and hazy weather is degraded and usually has low
contrast and poor visibility [18]. Some of the interesting
fields of application for image defogging algorithms are to
enhance the visibility of the vehicle visual system, which
can effectively prevent car accidents [19]. Some papers use
a physical imaging model based on the atmospheric
scattering phenomenon for image defogging [20, 21]. Many
improved defogging algorithms for unmanned surface
vehicle visual system are based on the physical model for
outdoor scenes [22-26]. In past years, some institutions
have done research on image defogging and obtained good
results. NASA has studied the image enhancement and
defogging algorithm since 1995, and their research has
made a great contribution to the field of image
enhancement based on the Retinex theory. Their algorithms
can greatly enhance the visibility of an image acquired
under bad weather conditions, such as smoke, haze,
underwater, night or low illumination conditions [26-30].
5. IMAGE ENHANCEMENT OF INFRARED
IMAGES
In this section, it will be provided a survey of
enhancement of infrared images with two spatial and
spatiotemporal homomorphic filtering algorithms based on
infrared imaging model. The Wavelength of infrared light
is longer than of visible light, the visible red light has
wavelength of 0.74 micrometers at the end of visible
spectrum, infrared light has 300 micrometers wide
wavelength band startng from 0.74 micrometers. These
wavelengths correspond to a frequency range of
approximately 1 to 400 THz [31]. Spatial and
spatiotemporal homomorphic filtering algorithms are
designed. The spatiotemporal homomorphic filter uses the
temporal information provided by the image sequence so
that an enhancement is faster than that obtained by utilizing
the spatial homomorphic filtering. STHF will spend less
time and iterations to compute a resulting image from a
similar degree of convergences the enhance images are in
general not good as those from SHF. Some authors suggest
using Wavelets for edge detection and as approximate
matched filters. Wavelets as edge detectors assume that
target edges on image differ in some way from clutter
edges [32, 33]. In some cases, a priori knowledge of target
is important in order to do scale separation [34]. Also,

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H. Ackar et al./ Southeast Europe Journal of Soft Computing Vol.8 No.1 March 2019 (42-48)
wavelets can be designed to function as approximate
matched filters [35].
6. CONTRASTENHANCEMENT
TECHNIQUES
Contrast is the visual difference that makes an object
distinguishable from the background and other objects.
Contranst enhancement techniques are changing pixel
intensitiy of the input imate in order to utilize maximum
possible bins. It has been an active research topic since the
early days of computer vision and digital image processing.
In this section, the focus will be on contrast enhancement
techniques for effective contrast enhancement. Most of the
researchers are focused on histogram-based contrast
enhancement techniques. Histogram based techniques, like
Homomorphic Filtering, are used to enhance the low
contrast for medical images [36]. In the literature can be
found modified versions of the hyperbolic algorithm for
contrast enhancement. These modified algorithm are
suitable for enhancement of magnetic resonance images.
This technique uses controlled fashion of the gray level
which is streching on whole image [37]. There are also
contrast enhancement techniques based on Fuzzy
techniques. Fuzzy techniques can manage imperfectness of
an image modeled as the uncertainty in the image. Fuzzy
method for contrast enhancement can be divided into three
stages, like classic fuzzy system: fuzzification, modification
of membership functions and defuzzification of the image
[38]. In the literature can be found an automatic methods
for contrast enhancement. For example, by grouping the
histogram components of a low-contrast image into a
proper number of bins according to a selected criterion,
then redistribute these bins uniformly over the grayscale.
The last step is to ungroup the previously grouped gray-
levels. This technique is known as gray-level grouping
(GLG) [39, 40]. Some authors propose an extension of
histogram equalization. With this approach, it is possible to
have independent histogram equalizations for two sub-
images found by decomposing the input image based on its
mean value. Resulting equalized sub-images are bounded
by each other around the input mean. This approach has a
great geature, it maintains the mean brightness of given
input images significantly [41].
7. VII. HAZE VISIBILITY
ENHANCEMENT OF IMAGES
The haze is the most common real-world
phenomena caused by atmospheric particles. Images
captured in hazy scenes suffers from visibility degradation
and significant reduction of contrast. Recovering scenes
from hazy images can be critical for image processing and
computer vision algorithms. Algorithms for haze-free
photographs are developed because consumers want a clear
visual content when they are shooting target objects or
landscapes. The floating particles in the atmosphere are
absorbing and scattering light in the enviroment, because of
this scattering and absorption we have degradation in hazy
images. This scattering and absorption reduce the direct
transmission of the light from the scene to the camera. The
attenuated direct transmission causes the intensity from the
scene to be weaker, while the airlight causes the appearance
of the scene to be washed out [42]. The one of the earliest
methods to analyze images of scenes captured in scattering
media is to extract scene depth by exploiting the presence
of the atmospheric scattering effects [43]. The methods
proposed in [44] does not assume that the haze-free image
is provided. These two methods are pioneers in dealing
with atmospheric particles. Some methods for haze
enhancement uses different base for enhancement like
multiple images, polarizing filters, known depth and single-
image. The multiple images methods uses multiple images
of same scene taken in different hazy conditions. With the
assumption that images are taken from the same scene, they
are sharing same color of atmospheric light but they have a
different direct transmission of colors. From this two planes
can be formed in the RGB space that intersect each other.
In [45] is presented how to estimate the atmospheric light
sith this intersect, which is similar to [46] for estimating a
light color from specular reflection. For methods with
known depth is proposed several algorithms based on a
single input image and they requires some user interaction
[47]. The first method from this paper requires from user to
select a region with less haze and region with more haze of
the same reflection as the first one's. From these inputs,
proposed algorithm can calculate resulting image and
dehaze hazy pixels. The second method asks user to
indicate vanishing point and to input the maximum and
minimum distance from the camera. This information is
used to interpolate the distance to estiamte the clear secene
between. In the paper [48] is proposed a framework for
contrast enhnacement of images taken in a vehicle.
8. LATEST IMAGE ENHANCEMENT
TECHNIQUES
The latest image enhnacement technques can be
devided into two groups, heuristic and classic image
enhancement techniques. In first part of this chapter, it will
be given review about novel classic image enhancement
techniques and after that it will be given review about
heuristic image enhancement techniques. One of the latest
classic algorithms for contours detectin in thermal images is
based on theory of sampling Kantorovich operators and on
analysis of the histogram of the enhanced thermographic
image [49]. Contrast Enhancement is an important step for
the analysis of microscopy images. In paper [50] is
proposed groundbrakeing design of the Phase Contrast
Microscopy Framework. The proposed image enhnacement
framework transforms the changes in image phase into the
variations of magnitude to enhance the structural details of
the image and to improve visibility. In paper [51] is
proposed algorithm for image enhancing approach for
transforming dark images into lightened scenes. This

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H. Ackar et al./ Southeast Europe Journal of Soft Computing Vol.8 No.1 March 2019 (42-48)
approach uses classical color transfer method to obtain first
order statistics from a target image and transfer them to a
dark input by modifying its hue and brightness. This
method can be used as a preprocessing step in order to
improve the recognition and interpretation of dark imagery
in a wide range of applications. Remote sensing images
often suffer low contrast, and the efficiency and robustness
of contrast enhancement. In the paper [52] is proposed
improved adaptive contrast enhancement method based on
histogram compacting transform. In the papers [53, 54] are
presented low-lightning image enhancement based on
Retinex model. The different Retinex models are proposed
in order to enhance illumination and reflectance. New
image enhnacement method based on Nonsubsampled
Contourlet Transform is proposed in paper [55]. In this
paper is presented contourlet transform as an extension of
the wavelet transform that provides a multi-resolution and
multi-direction analysis for two dimensional images. In
the paper [56] is presented a high-resolution image
enhancement wavelet-based algorithm for edge smoothness
of an Satelite Image. The proposed algorithm apply the
three-level discrete wavelet transform and compute the
output of the algorithm. In order to improve contrast and
restore color for underwater images without suffering from
insufficient details and color cast, in the paper [57] is
proposed a fusion algorithm for different color spaces based
on contrast limited adaptive histogram equalization.
In order to have an effective colour enhancement
framework for statistical and logarithmic image processing
enhancement algorithms, the paper [58] proposes approach
of utilizing the fusion of partial, multiple computed
luminance channels with colour image channel statistics
obtained from the input colour image for adaptive color
enhancement. The Unmanned Aerial Vehicles are widely
used for capturing images in border area surveillance,
disaster intensity monitoring, etc. In the paper [59] is
presented usage of Firefly Algorithm in order to enhance
aerial images taken by a Mini Unmanned Aerial Vehicle
via optimizing the value of certain parameters. For Smart
City applications it is crucial to have a good pre-processing
technique for infrared image enhancement. Existing
grayscale mapping-based algorithms always suffer from
over-enhancement of the background, noise amplicifation
and brightness distortion. In the paper [60] is proposed an
adaptive histogram partition and brightness correction in
order to correct above mentioned problems.
In the paper [61] are proposed two novel
histogram based image enhancement algorithms. The
proposed algorithms provide the way to control the
brightness and contrast of enhanced image by adjusting the
parameters. After classic image enhancement techniques,
the novel huristic methods will be described. Low-light is a
challenging enviroment for image processing. In the paper
[62] is proposed method for low-light image enhancement
using Gaussian Process and Convolutional Neural Network.
In the papers [63, 64] are proposed image enhancement
techniques using biologically inspired artificial Bee Colony
Algorithms. In this papers image constrast enhnacement is
considered as an optimization problem and the artificial bee
colony algorithm is utilized to find the optimal solution for
this optimization problem. In the paper [65] can be found a
survay on Nature-Inspired optimization algorithms and
their application in image enhancement domain. Single
image contrast enhancement methods are used to adjust the
tone curve to correct the contrast of an input image. In the
paper [66] is proposed learning algorithm of convolutional
neural network to train a single image contrast enhancer. In
the paper [67] is proposed a deep learning neural network
with purpose of obtaining enhanced representations of the
sequences for visual odometry. After initial results, it is
proposed a reduced size of convolutional neural network
for faster computation. In the paper [68] is propsoed
Learned Perceptual Image Enhancement technique. The
two main contributions of this paper are presenting state of
the art predictor that encompasses several aspects of human
perceptual preferences and the researchers uses neural
image asessment as a perceptual loss for image
enhancement tasks.
9. CONCLUSION
Image enhancement techniques change images to
provide a better representation of the information
encapsulated in the image. In this paper is presented a
review for various fields of image enhancement. For every
purpose like underwater imaging or medical images, there
are different algorithms and techniques suitable for image
enhancement. As we can see from this review paper, and
also from other review papers, there is no universal image
enhancement technique, the most important reason for that
is the fact that there are a lot of different factors, for
example, fogg in images and videos, or the fact that
medical images are mostly grayscale images.
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