International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 4 - September 2015
Image Restoration and Comparative Analysis
Parul Gupta1, Rajesh Mehra2
1
M.E. student Department of Electronics & Comm. Engineering,
National Institute of Technical Teachers Training & Research Center/Panjab University, Chandigarh, India
2
Associate Prof. Department of Electronics & Comm. Engineering,
National Institute of Technical Teachers Training & Research Center / Panjab University, Chandigarh, India
ABSTRACT- Digital images suffer blurring from
various known and unknown point spread functions.
Image restoration from degraded image is very big
problem in image processing. Image restoration is a
process by which a noisy or degraded image is
converted into nearly an original image. Improvement
in the appearance of the image is done by image
restoration. In this paper a Memetic Algorithm is used
to restore different simulated images. The algorithm
runs for each position of whole image. The algorithm
converges to a globally optimal restoration. Algorithm
is based on extended neighbourhood search, so that
local search area optimize to achieve high PSNR in less
number of iteration. To narrow the solution space
population is initialized by hill climbing method. To
evaluate the proposed results a number of experiments
have been done and quantitatively different standard
parameters are employed that are PSNR, UIQI and Q
factor.
Keywords:
Memetic
restoration,fitness function
algorithm,
image
I. INTRODUCTION
The imaging systems are not ideal. They are having
some imperfections because of non-ideal conditions.
The observed image by these imaging systems
represents the degraded version of original image.
Image degradation at the time of acquisition making an
image worthless so, no further analysis can be process
on this acquired degraded image. Image can be
degraded by blur or noise [1, 2]. An image can be
degraded by many factors which can be introduced in
the form of motion & Gaussian blur and Gaussian
noise. The main problem in image restoration is that the
images are ill posed in nature, means a small change in
input can lead to a large change in output [3]. A
degraded image can lose some very important or useful
information and the quality of image is also degraded .
Usually blur is having low frequency components and
noise is having high frequency component, so making
restoration problem more complicated because a single
filter cannot eliminate both the factors. A low pass filter
will suppress the noise term but in this case the blur
will be boost up and in case of high pass filter the
opposite phenomena will take place. There is a trade off
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between image deblurring and noise smoothening [4].
Discrete wavelet transform is using as an standard tool
to code the images in image processing.
So many restoration methods are used in past decades.
Image restoration is always been a active research area
for the researchers. However the majority of degraded
functions are not linear but for formulation it is
assumed that degradation functions are linear and
image restoration is assumed to be linear in most of the
application. It can be modelled as two dimensional
convolution between original image and the point
spread function(PSF) [5]. Mathematically the
degradation model is modelled as
(1)
Where g(x, y) denote the degraded image ,f(x, y)
denote the original image, h(x, y) denote the PSF ,n(x,
y) represents the additive white Gaussian noise
assuming zero mean and vaiance σ2 and * denote the
convolution in two dimension. The degraded image[6]
in
matrix
form
can
be
written
as
(2)
Original Image
f(x, y)
+
Gaussian
Filter
Degraded
/Blurred Image
g(x ,y)
Gaussian Noise
Fig. 1 Degradation Model
II.RESTORATION TECHNIQUES
To recover the original image from the degraded image
a number of methods are applied in past decades. The
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 4 - September 2015
Wiener filter is one of the most efficient and basic
method to restore the images. However most of the time
the reason for the degradation is not known so
degradation function cannot be expressed by a linear
function. Most of the time the reason
for the
degradation is not known and one has to guess the
initial value , the restoration of image in this case is
known as blind image restoration. The degradation
function cannot be express in linear function form,
blind restoration methods face complexity to solve the
problem [7]. Linear degradation function applied to the
observed image that is corrupted by non linear function
cause serious problems like edges cannot be preserved
and quality of image will degrade [8].
To preserve the edges of the image a number of
regularization techniques have been employed. Edges
of the image are smoothened by regularization and
adaptive constrained optimization. After that a PSO
based technique is used to preserve the edges of the
image [2 3]. Edges are also efficiently improved by
Sobel Edge Detection [9]. Most of the techniques to
restore the images suffer from large computation time.
This computation time is minimized by High speed CT
image reconstruction using FPGA [10]. Temporal
average filtering is used for efficient noise removal but
the drawback is ,it requires large number of time frames
[11].
Now a day to restore the degraded image efficiently,
restoration problem is treated as optimization problem.
Efficient restore result are obtained by minimizing the
cost function. So many optimization techniques have
been employed for image restoration. Optimization
algorithms can be represent in two forms one is
deterministic and the second one is probabilistic. The
characteristic of the solution and their use in problem,
having direct relation in deterministic optimization,
while in probabilistic only some of the elements are
consider from the search space and further
computations are processed on it [12, 13].
Genetic algorithm is one of the early optimization
technique based on meta heuristics. Individual selection
in initial population and size of population are the
main success of Genetic algorithm. Good initialization
population gives good result while poor initialization of
population gives poor result, resulting the Genetic
algorithm to converge prematurely. This premature
convergence is one of the inherent characteristic of
Genetic algorithm. This characteristic of Genetic
algorithm makes it incapable to find the solution of
numerous problems [14, 15].
Steps of Genetic Algorithm are given below.
1) Initialization includes random generation of a
population of chromosome.
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2) Selection of individuals are based on their fitness
value. Chances of selection are more for more fit
individual.
3) Reproduction applies the crossover and mutation on
selected individual.
Fig. 2 Flow steps of Genetic Algorithm
4) Replacement of the old population Individual by the
new one.
Fig. 3 flow Chart of Memetic Algorithm
The extension of traditionally genetic algorithm is
called Memetic Algorithm. To reduce the premature
convergence of the likelihood a local search technique
is used. The local search can be use at any phase or
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stage in algorithm to optimize the results. Exploitation
and exploration are two significant issues in the search
technique. To use available existing knowledge to find
out the the better solution is come under exploitation
and to investigate unknown and new area in search
space is come under exploration. Genetic algorithm is
having high Exploration power and local search is
having high exploitation power. Balance between
exploitation and Exploration is made by Memetic
Algorithm[14 15]. Genetic and Memetic algorithms are
non traditional optimization method to find robust and
efficient solution of the problem. Instead of reaching to
the global optimum, the aim of Memetic Algorithm is
to find sufficient „good‟ solution
according to the
characteristics of the problem [14].
vector of fixed length . Each bit (
corresponds to position of a particular gene . The third
part, , is a positive real vector of fixed length and
corresponds to the particular gene and selection is done
on basis of this value.
For different system models, the algorithm may be
completely different. In response to these problems new
image restoration algorithms are generally based on
Memetic algorithm. In the proposed method Memetic
algorithm does not depend
only upon
the
neighbourhood characteristics like the traditional
method for processing but the whole image is
considered and the effect of processed image is close to
the observation of human visual effects.
III. PROPOSED TECHNIQUE
Different stages of proposed technique are shown in fig.
4. The proposed technique is performed on different
matlab simulated test images. The Memetic Algorithm
for parent Selection first make an initial population P
from the local search area and then do a number of
generations. There are many different ways to generate
the individual parent of the initial population. In our
case, each individual parent is randomly generated by
hill climbing method so that their intensity values falls
within local search area limit. After each generation, the
best new child population replaces the previous parent
population P. The new child population P′ is generated
from previous population P in the following way.
Best individuals of P are copied to P‟ according to their
fitness value. The fitness value measures the best
individuals that are persevered along the generations.
The remaining of P′ is obtain by crossover and local
search.
Procedural steps
Converting image
Fig.4 Design steps of Proposed Algorithm
1) Calculate fitness value of each individual. Fitness
value is used to initialize the population with in local
search area.
is used to denote the fitness value.
2) The new subset strength vector of each individual is
updated locally and is as follows
into a column matrix
,
where
and
denote the gene
subset vector and ranking coefficient vector and
denote the gene subset strength. Individuals are selected
randomly
by hill climbing method from
,The first and second part, and are a
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(3)
Where is the 0 to 255 gene subset strength and
denote the step size globally with in range
selected randomly by extended neighbourhood search
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 4 - September 2015
for each dimension. For each iteration
physical component.
will be the
3) The selection of each individual is calculated
globally with in limit and is as follows.
(4)
4) The process repeated till best
several number of iteration.
is not achieved in
a
b
5) The global best value of each individual is calculated
by evaluation of elimination process.
c
d
Fig.5 Original Test Images a) Squares, b)Peppers, c) Cameraman
,d)
Barbara
(5)
IV. RESULT ANALYSIS
The proposed method is implemented on four simulated
images are taken from matlab data set. firstly the
different four simulated images are passing through a
low pass filter with 5 x 5 Gaussian blur with standard
deviation of σ = 1.6 later a Gaussian noise(SNR=20dB)
is added in the blurred image.
The simulation is done on standard test images of
“cameraman”, “barbara”, “squares” and “peppers”. In
this section the performance is evaluated by three
factors.
1) PSNR
2) Universal image quality index (UIQI)
3) Metric Q
The ideal values of PSNR and UIQI are +
and 1 respectively
The simulations are performed on 256 x 256 images.
Fig 5 shows the original test images. fig. 6 shows noisy
images corrupted by 5x5 Gaussian blur with standard
deviation σ = 1.6 and the random Gaussian noise. The
produced results are stored in fig. 7.
Fig.6 Noisy test images by Gaussian blur 5x5 and
sigma=2.1 and adding Gaussian random noise(SNR=20dB) a)
Squares, b) Peppers, c) Cameraman , d) Barbara
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International Journal of Engineering Trends and Technology (IJETT) – Volume 27 Number 4 - September 2015
problems. This paper presented a new blind restoration
approach to deblur the remote sensing images. Genetic
Algorithm performs good for the exploration of search
space but convergence process is slow. Local search
techniques are good in exploitation, so the convergence
is fast. By hybridization of these two ,the performance
can be improve. In this paper the hybridization of
extended neighbourhood search is applied of local
search of Genetic Algorithm is applied. The proposed
method has shown the percentage improvement in
PSNR is approximately 37% for all four test images
that are used in paper. The proposed restoration method
has considerable amount of computational complexity
in terms of speed. Processing speed of the proposed
method could be improved in future work.
REFERENCES
Fig.7 Restored test images by proposed method a) squares,
b)peppers, c) cameraman ,d) Barbara
Fig. 7 shows the restored images and it can be seen that
proposed method is able to restore the images with less
noise or this method is less sensitive to noise.Table 1.
shows comparative and evaluated results. One can
easily see that in this proposed method PSNR as well as
UIQI index are having higher values in presence of
noise. Q index is also having similar analysis pattern as
like UIQI indices and PSNR and proposed method
validates its good performance. Therefore for the
evaluation of the real images also this proposed method
could be use.
TABLE 1
PERFORMANCE ANALYSIS OF DIFFERENT TEST IMAGES
IMAGE
BEFORE
RESTORATION
PSNR
UIQI
AFTER
RESTORATION
Q
PSNR
UIQI
Q
Square
20.30
0.076
0.032
29.37
0.43
35.12
Pepper
19.70
0.062
0.048
26.64
0.24
13.23
Cameraman
18.38
0.054
0.066
25.51
0.31
33.98
Barbara
18.75
0.067
0.76
25.87
0.41
36.23
V. CONCLUSION
The information about the degradation function is not
known in most of the cases. Image restoration for these
cases required very complex tools to solve these
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Authors:
Dr. Rajesh Mehra: Dr. Mehra is currently
associated with Electronics and Communication Engineering
Department of National Institute of Technical Teachers‟
Training & Research, Chandigarh, India since 1996. He has
received his Doctor of Philosophy in Engineering and
Technology from Panjab University, Chandigarh, India in
2015. Dr. Mehra received his Master of Engineering from
Panjab Univeristy, Chandigarh, India in 2008 and Bachelor of
Technology from NIT, Jalandhar, India in 1994. Dr. Mehra
has 20 years of academic and industry experience. He has
more than 320 papers to his credit which are published in
refereed International Journals and Conferences. Dr. Mehra
has guided 70 ME thesis and he is also guiding 02
independent PhD scholars in his research areas. He has also
authored one book on PLC & SCADA. He has developed 06
video films in VLSI area. His research areas are Advanced
Digital Signal Processing, VLSI Design, FPGA System
Design, Embedded System Design, and Wireless & Mobile
Communication. Dr. Mehra is member of IEEE and ISTE.
Mrs. Parul Gupta: Mrs. Parul is currently
pursuing M.E from National Institute of Technical Teachers
Training & Research Chandigarh, India. She has completed
B.Tech from I.E.T. M.J.P. Rohilkhand University Bareilly
(U.P.). She is having six years of teaching experience in
Institute of Technology & Management GIDA, Gorakhpur
(U.P.). Mrs. Parul‟s interest areas are Image processing,
VLSI, Wireless and Mobile Communication and Digital
Electronics.
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