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Copyright © 2013 IJECCE, All right reserved
270
International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
A Review on Image Processing
Amandeep Kour
Deptt. of CSE,
Lovely Professional
University, Punjab
Vimal Kishore Yadav
Deptt. of Energy Science
and Engineering,
Indian Institute of Technology,
Bombay, India
Vikas Maheshwari
Deptt. of ECE,
School of Engg. and Technology
Apeejay Stya University, Sohna,
Gurgaon, Haryana, India
Deepak Prashar
Deptt. of ECE,
Lovely Professional
University, Punjab
Abstract - Image Processing includes changing the nature
of an image in order to improve its pictorial information for
human interpretation, for autonomous machine perception.
Digital image processing is a subset of the electronic domain
wherein the image is converted to an array of small integers,
called pixels, representing a physical quantity such as scene
radiance, stored in a digital memory, and processed by
computer or other digital hardware. Interest in digital image
processing methods stems from two principals applications
areas: improvement of pictorial information for human
interpretation; and processing of image data for storage,
transmission, and representation for autonomous machine
perception. Edges characterize boundaries and edge
detection is one of the most difficult tasks in image processing
hence it is a problem of fundamental importance in image
processing. In this paper investigates different steps of digital
image processing.like, a high-speed non-linear Adaptive
median filter implementation is presented. Then Adaptive
Median Filter solves the dual purpose of removing the
impulse noise from the image and reducing distortion in the
image.
The Image Processing Toolbox software is a collection of
functions that extend the capability of the MATLAB numeric
computing environment. The toolbox supports a wide range
of image processing operations on the given image.
Keywords - Image Enhancement, Feature Extraction.
I. INTRODUCTION
With the advent of electronic medium, especially
computer, society is increasingly dependent on computer
for processing, storage and transmission of information.
Computer plays an important role in every parts of today
life and society in modern civilization. With increasing
technology, man becomes involved with computer as the
leader of this technological age and the technological
revolution has taken place all over the world based on it. It
has opened a new age for humankind to enter into a new
world, commonly known as the technological world.
Computer vision is a part of everyday life. One of the most
important goals of computer vision is to achieve visual
recognition ability comparable to that of human [1], [2],
[3].Among many recognition subjects, face recognition
has drawn considerable interest and attention from many
researchers for the last two decades because of its potential
applications, such as in the areas of surveillance, secure
trading terminals, Closed Circuit Television (CCTV)
control, user authentication, HCI Human Computer
Interface, intelligent robot and so on. A number of face
recognition methods have been proposed [4], [5] and some
related face recognition systems have been developed. In
this paper the computational model of face recognition,
which is fast, reasonably simple, and accurate in
constrained environments such as an office or a household
is compared. These approaches have advantages over the
other face recognition schemes in its speed and simplicity,
learning capacity and relative insensitivity to small or
gradual changes in the face image. The first step in face
recognition is the acquisition of faces in visual media.
Face acquisition for the purposes of Recognition requires
not only face detection, but precise alignment prior to
matching faces. We perform this alignment
automatically through pose estimation and landmark
localization.
II. DIFFERENT APPROACH
Image Processing: Digital image processing means that
it processing of images which are digital in nature by a
digital computer. It is motivated by three major application
first one is improvement of pictorial information for
human perceptions means whatever image you get we
wants to enhance the quality of the image so that image
will have better look. Second application is for
autonomous machine application this has various
applications in industry particularly for quality control and
assembly automations. Third applications is efficient
storage and transmission for example if we wants to store
the image on our computer this image will need certain
amount of space on our hard disk so we use some
technique so that disk space for image will required less.
Image processing is any form of signal processing for
which the input is an image. Digital image processing is
the study of representation and manipulation of pictorial
information by a computer. Image Processing Toolbox
supports images generated by a wide range of devices,
including digital cameras, frame grabbers, satellite and
airborne sensors, medical imaging devices, microscopes,
telescopes, and other scientific instruments. It can
visualize, analyze, and process these images in many data
types, including single- and double precision floating-
point and signed or unsigned 8-, 16-, and 32-bit integers.
There are several ways to import or export images into and
out of the MATLAB environment for processing. It can
use Image Acquisition Toolbox (available separately) to
acquire live images from Web cameras, frame grabbers,
DCAM-compatible cameras, and other devices. Using
Database Toolbox (also available separately), it can access
images stored in ODBC/JDBC-compliant databases.
MATLAB supports standard data and image formats,
including JPEG, TIFF, PNG, HDF, HDF-EOS, FITS,
Microsoft Excel, ASCII, and binary files. It also supports
multiband image formats, such as LANDSAT. Low-level
I/O functions enables to develop custom routines for
Copyright © 2013 IJECCE, All right reserved
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International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
working with any data format. Image Processing Toolbox
supports a number of specialized image file formats.
Image as input: The system needs some types of images
as the input. These images require the process of computer
algorithms as per the input image. These computer
algorithms yield two types of images from Computer
Algorithm: noisy image and magnitude image.
Size of images: There are two sizes of images that we can
use, that are (512 X 512) inches, (256 X 256) inches, and
(1024 X 1024) inches.
Image file formats: A Variety of image file formats are
available at present. Like TIFF, JPEG, GIF, BMP, etc. We
mainly using TIFF and JPEG here, these are explained as
follows:-
TIFF- stands for Tagged Image File Format. Its extension
is recognized both as „tif‟ and „tiff‟. These are the file
formats used for storing images, including photographs
and line art. It grew to accommodate greyscale images,
then colour images. Today, it is a popular format for high-
colour-depth images, along with JPEG.
JPEG- stands for Joint Photographic Experts Group. It has
„.jpg‟, „jpeg‟ as the allowed extensions. It is the most
common format for storing and transmitting photographic
images on the World Wide Web and is a commonly used
method of compression for photographic images.
Fig.1. .JPG Image Format
Images types: Four types of images are there:
Intensity images – An intensity image is a data matrix
whose values represent Intensities within some range. For
the elements of class uint8 or class uint16 of an intensity
image, the integer values lie between (0,255) and [0,
65535], respectively. And if the image is of class double,
then the associated values are floating-point numbers.
Conventionally, the intensity images with scaled, class
double data type have a range of [0, 1]. In MATLAB, an
intensity image is stored as a single matrix, with each
element of the matrix corresponding to one image pixel.
Fig.2. GRAY Image Format
Binary images - A binary image is a logical array of 0s
and 1s. Pixels with the value 0 are displayed as black;
pixels with the value 1 are displayed as white. In
MATLAB, a binary image must be of class logical that is
why the intensity images that happen to contain only 0‟s
and 1‟s are not taken as binary images[6].
Fig.3. BW Image Format .This gives image in Black and
White.
Indexed images – An indexed image consists of a data
matrix, X, and a Colour map matrix termed as “map”. The
“map” is an m-by-3 array of class double containing
floating-point values in the range [0, 1]. Its every row
specifies the red, green, and blue components of a single
colour. For these images pixel values are directly mapped
to their corresponding colour map values [7].
Figure 4: Indexed Image (red) Format
RGB images- An RGB image is also referred as a true-
colour image. In MATLAB these images are stored in the
form of an m-by-n-by-3 data array that defines red, green,
and blue components for each individual pixel. The colour
of each pixel is determined by the combination of the red,
green and blue intensities stored in each colour plane at
the pixel‟s location. Graphics file formats store RGB
images as 24-bit images, where the red, green and blue
components are 8 bits each. An RGB array can be of class
double, uint8, or uint16. In an RGB array of class double,
each colour component is a value between 0 and 1. A pixel
whose colour components are (0,0,0 ) is displayed as
black, and a pixel whose colour components are (1,1,1 ) is
displayed as white. The three colour components for each
pixel are stored along the third dimension of the data
array.
Figure 5: RGB Image Format
Resolution: Similar to one-dimensional time signal,
sampling for images is done in the spatial domain, and
quantization is done for the brightness values. In the
Sampling process, the domain of images is divided into N
rows and M columns. The region of interaction of a row
and a Column is known as pixel. The value assigned to
each pixel is the average brightness of the regions. The
position of each pixel is represented by a pair of
coordinates (xi, xj). The resolution of a digital signal is the
number of pixel is the number of pixel presented in the
number of columns × number of rows. For example, an
Copyright © 2013 IJECCE, All right reserved
272
International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
image with a resolution of 640×480 means that it display
640 pixels on each of the 480 rows. Some other common
resolution used is 800×600 and 1024×728. Resolution is
one of most commonly used ways to describe the image
quantity of digital camera or other optical equipment. The
resolution of a display system or printing equipment is
often expressed in number of dots per inch. For example,
the resolution of a display system is 72 dots per inch (dpi)
or dots per cm.
Pre and Post-Processing Images: Image Processing
Toolbox provides reference-standard algorithms for pre-
and post processing tasks that solve frequent system
problems, such as interfering noise, low dynamic range,
out-of-focus optics, and the difference in colour
representation between input and output devices.
Image processing operations: Image processing
operations can be roughly divided into three major
categories:
A) Image Restoration
B) Image Enhancement
C) Remove “noise” from an image
D) Remove motion blur from an image.
E) Image Compression
F) Image Segmentation
G) Feature extraction
H) Image transformation
A) Image Restoration: Restoration takes a corrupted image
and attempts to recreate a clean image. As many sensors
are subject to noise, they results in corrupted images that
don‟t reflect the real world scene accurately and old
photograph and film archives often show considerable
damage.
Thus image restoration is important for two main
applications:
a) Removing sensor noise,
b) Restoring old, archived film and images.
It is clearly explained in the figure 6 and figure 7.
Fig.6. Original image Fig.7. Image after restoration
B) Image Enhancement Image enhancement techniques
in Image Processing Toolbox enable to increase the signal-
to-noise ratio and accentuate image features by modifying
the colours or intensities of an image. It can:
a) Perform histogram equalization
b) Perform de correlation stretching
c) Remap the dynamic range
d) Adjust the gamma value
e) Perform linear, median, or adaptive filtering
The toolbox includes specialized filtering routines and a
generalized multidimensional filtering function that
handles integer image types, multiple boundary padding
options, and convolution and correlation. Predefined filters
and functions for designing and implementing its own
linear filters are also provided.
a) Histogram equalization: Its one of the step used in
image processing so that the image [8] contrast through
should be uniformed. Image after histogram equalization
Fig.8. Histogram Equlization
b) De-correlation Stretch: The de-correlation stretch is
a process that is used to enhance (stretch) the colour
differences found in a colour image. The method used to
do this includes the removal of the inter-channel
correlation found in the input pixels; hence, the term "de-
correlation stretch"
The purpose of this document is to explain:
a) The conditions that normally appear in multispectral
data that indicate that colour enhancement is needed,
b) How a de-correlation stretch addresses those needs,
c) How a de-correlation stretch works, i.e.,
computationally, what steps are performed,
d) What the limitations are to this approach.
c) Remap the dynamic range: In image processing,
computer graphics, and photography, high-dynamic-range
imaging (HDRI or just HDR) is a set of techniques that
allow a greater dynamic range of luminance between the
lightest and darkest areas of an image than current
standard digital imaging techniques or photographic
methods. This wide dynamic range allows HDR images to
more accurately represent the range of intensity levels
found in real scenes, ranging from direct sunlight to faint
starlight.[9]
The two main sources of HDR imagery are computer
renderings and merging of multiple photographs, the latter
of which in turn are individually referred to as low
dynamic range (LDR)[10] or standard dynamic range
(SDR)[11] photographs.
Tone-mapping techniques, which reduce overall contrast
to facilitate display of HDR images on devices with lower
dynamic range, can be applied to produce images with
preserved or exaggerated local contrast for artistic effect.
Merged to HDR then reduced to LDR
Fig.9. Local tone mapping Fig.10. Simple contrast
reduction
d) Adjust the gamma value: Basically: Gamma is the
relationship between the brightness of a pixel as it appears
on the screen, and the numerical value of that pixel. You
Copyright © 2013 IJECCE, All right reserved
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International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
probably already know that a pixel can have any 'value' of
Red, Green, and Blue between 0 and 255, and you would
therefore think that a pixel value of 127 would appear as
half of the maximum possible brightness, and that a value
of 64 would represent one-quarter brightness, and so on.
Well, that's just not the case, I'm afraid.Here's an example
of the effect that a change in gamma can have on the
appearance of an image.
Fig.11. (a) Left (b) Centre (c) Right
On the left is the image as it might appear on an un-
corrected monitor. The centre image should look right on a
monitor with a gamma of around 1.8, and lastly;
Right-hand image is how a system with a linear response
[gamma of 1.0] might display the image.
Notice how the colour saturation and hue change with the
gamma?
What this means is that if your monitor gamma isn't set
correctly, then you haven't a hope of seeing colours and
tones the way that they'll appear on other people's
monitors; and they won't see your images the way that you
intended either.
e) Median Filter: Median filtering is a non-linear, low-
pass filtering method, which you use to remove "speckle"
noise from an image. A median filter can outperform
linear, low-pass filters on this type of noisy image because
it can potentially remove all the noise without affecting the
"clean" pixels. Median filters remove isolated pixels,
whether they are bright or dark.
Fig.12. Implementation of a 3 × 3 median filter window
C) Remove “noise” from an image: Noise is
considered to be any measurement that is not part of the
phenomena of interest. Noise can be generated within the
electrical components of the Input amplifier (internal
noise), or it can be added to the signal as it travels down
the input wires to the amplifier (external noise). There are
various types of noises available in MATLAB: „Gaussian‟,
„Poisson‟, and „Speckle‟, „localvar ‟, „Salt & pepper
(Median). Noise is added to the input images to check
their various parameters. Here noise is removing from
image.
D) Deblurring Images: Image Processing Toolbox
supports several fundamental deblurring algorithms,
including blind, Lucy-Richardson, Wiener, and
regularized filter de-convolution, as well as conversions
between point spread and optical transfer functions. These
functions help correct blurring caused by out-of-focus
optics, movement by the camera or the subject during
image capture, atmospheric conditions, short exposure
time, and other factors. All deblurring functions work with
multidimensional images.
E) Image Compression: To store these images, and
make them available over network (e.g. the internet),
compression techniques are needed. Image compression
addresses the problem of reducing the amount of data
required to represent a digital image.
Need for compression
The following example illustrates the need for
compression of digital images.
a) To store a colour image of a moderate size, e.g.
512×512 pixels, one needs 0.75 MB of disk space.
b) A 35mm digital slide with a resolution of 12 μm
requires 18 MB.
c) One second of digital PAL (Phase Alternation Line)
video requires 27 MB.
An[14] image, 1024 pixel×1024 pixel×24 bit, without
compression, would require 3 MB of storage and 7
minutes for transmission, utilizing a high speed, 64
Kbits/s, ISDN line. If the image is compressed at a 10:1
compression ratio, the storage requirement is reduced to
300 KB and the transmission time drop to less than 6
seconds. Types of compression [14]:-
Lossless coding techniques:
a) Run length encoding
b) Huffman encoding
c) Arithmetic encoding
d) Entropy coding
e) Area coding
Loss coding techniques:
a) Predictive coding
b) Transform coding (FT/DCT/Wavelets)
F) Image Segmentation: Main goal of image
segmentation is to divide an image into parts that have a
strong correlation with objects or areas of the real world
contained in the image. In image processing useful pixels
in the image are separated from the rest. The result of
image segmentation is a set of segments that collectively
cover the entire image, or a set of contours extracted from
the image.
Copyright © 2013 IJECCE, All right reserved
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International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
a) Original image b) result of k-means segments
c) final segments after segmentation
Fig.13. Perceptual Image Segmentation Algorithm
a) Threshold segmentation techniques
b) Edge detection segmentation techniques.
Edge detection:
Several methods of edge detection exist in practical. The
procedure for determining edges of an image is similar
everywhere but only difference is the use of masks.
Different types of masks can be applied such as
a) Sobel
b) Prewitt
c) Kirsch
d) Canny
Motivation behind Edge Detection: The purpose of
detecting sharp changes in image brightness is to capture
important events and changes in properties of the world.
For an image formation model, discontinuities in image
brightness are likely to correspond to:-
a) Discontinuities in depth
b) Discontinuities in surface orientation
c) Changes in material properties
d) Variations in scene illumination
a) Sobel mask method b) Canny mask method
Fig.14. Edge-detection
Thresholding: Once we have computed a measure of
edge strength (typically the gradient magnitude), the next
stage is to apply a threshold, to decide whether edges are
present or not at an image point. The lower the threshold,
the more edges will be detected, and the result will be
increasingly susceptible to noise and detecting edges of
irrelevant features in the image. Conversely a high
threshold may miss subtle edges, or result in fragmented
edges.
G) Feature Extraction [12]: Two methods are discussed
here
a) To extract features of a face at first the image is
converted into a binary. From this binary image the
centroid (X, Y) of
The face image is calculated using equation 1 and 2.
=
(1)
=
(2)
Where x, y is the co-ordinate values and m=f(x,y)=0
or1. Then from the centroid, only face has been cropped
and converted into the gray level and the features have
been collected.
b) Gabor filter- It is also one of the methods to extract
feature from image. Will give image features in eight
different angles and at five different frequencies. A Gabor
filter is a linear filter whose impulse response is defined
by a harmonic function multiplied by a Gaussian function.
Because of the multiplication-convolution property
(Convolution theorem), the Fourier transform of a Gabor
filter's impulse response is the convolution of the Fourier
transform of the harmonic function and the Fourier
transform of the Gaussian function [13]. A Gabor filter
can be applied to images to extract feature aligned at
particular orientation. The useful parameters of a Gabor
filter are orientation and frequency. The Gabor filter is
thought to mimic the sample cells in the visual cortex [8].
Here a Gabor filter bank is implemented on face images
with 8 different orientation and 5 different frequencies (in
Gabor wavelet transformation is done by convolution of
the image with the 40 Gabor filters). Formally the Gabor
filter is a Gaussian (with variances Sx and sy along x and
y-axes respectively) and is described by the
Equation: -Gabor filter = G(x,y,,f)
=1
2′
+
× cos2 × ×′ (3)
′= cos + sin
= cos sin
f: frequency
: The orientation
G: Output filter
Gabout: Output filtered image
Fig.15. Gabor filter of five frequencies and 8 orientations
h(x, y) : s(x, y)g(x, y)
s(x, y) : Complex sinusoid
g(x, y) : 2-D Gaussian shaped function, known as envelop.
A Gabor filter can be described by the following
parameters: the Sx‟ and Sy‟ of the Gaussian explain the
shape of the base (circle or ellipse), frequency (f) of the
sinusoid, orientation (Ө) of the applied sinusoid. Fig .2
show example of Gabor filters [14]. For example When
the size of image (27×18) convolution with the Gabor
filter (5×8), it becomes 145×144 but due to large size (or
large number of input in neural network) we reduce the
dimensions from 145×144 to 45×48. And then Convert
cell array of matrices to single matrix 2160×1 using
cell2mat() function. This single matrix goes to as a input
in neural network means that number of input in neural
network is 2160(neurons) for a single input image.
H) Image Transforms: Transforms such as FFT and
DCT play a critical role in many image processing tasks,
including image enhancement, analysis, restoration, and
Copyright © 2013 IJECCE, All right reserved
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International Journal of Electronics Communication and Computer Engineering
Volume 4, Issue 1, ISSN (Online): 2249–071X, ISSN (Print): 2278–4209
compression. Image Processing Toolbox provides several
image transforms, including DCT, Radon, and fan-beam
projection. It can reconstruct images from parallel-beam
and fan-beam projection data (common in tomography
applications). Image transforms are also available in
MATLAB and in Wavelet Toolbox (available separately).
III. RESULT AND DISCUSSION
The Image processing helps in the improvement of
pictorial information, which is easily interpreted by human
and image can easily stored, transmitted and represented
for autonomous machine perception. The different steps of
digital image processing can be done by implementation
of a high-speed non-linear Adaptive median filter .It also
solve the dual purpose of removing the impulse noise from
the image and reducing distortion in the image. The
capability of the MATLAB numeric computing
environment can be extended by Image Processing
Toolbox.
IV. CONCLUSION
Here we discuss different steps of image processing,
from the beginning where you taken simple image to every
processing steps of digital image processing. This
discussion evaluates the optional steps for each stage. In
this paper the fast, simple and accurate computational
model of face recognition is given. These approaches have
advantages over the other face recognition schemes in its
speed and simplicity, learning capacity and relative
insensitivity to small or gradual changes in the face image
the best you consider according to our objective .Like,
different edge detection process is given here and each one
have different characteristic. Similarly for feature
extraction here two processes used name centriod (X, Y)
and gabor filter technique. Whichever is considered is
depend upon on our objective.
IV. FUTURE SCOPE
We can implement the best algorithm to find best result
in according with noised, blurred and many unwanted
error contain in image. In image an important part is the
compression. Image compression reduces the amount of
data required to represent the image by using different
transform so its important to reduce the data size
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AUTHOR’S PROFILE
Amandeep Kour
passed B.E degree in information technology from
Mahant Bachittar Singh College of Engineering and
Technology, Jammu University,India in the year
2009. She received the diploma in Intrnational cross
cultural research and human resource management
from The Bussiness School, Jammu university, India
in the Year 2010. Presently she is persuing her M.Tech in computer
Science from Lovely Professional University , Jalandhar,india.Her
research intrest includes Face recognition using Template Matching.Her
2 paper is accepted in International Journals and 1 paper in International
Conference(IEEE).
Vimal Kishore Yadav
passed B.Tech degree in Electronics and engineering
from Anand Engineering college,Agra,U.P Technical
University Lucknow. He served as an Assistant
Proffesor in ECE department at Hindustan College
of Science and Technology Mathura. He has
published 2 Papers in International conference in
IEEE and 4 papers in International Journal. Presently
he is persuing his M.Tech in Energy Science and Engineering at IIT
Bombay, Mumbai, India.
