DOI 10.4010/2016.1625
ISSN 2321 3361 © 2016 IJESC
`
Research Article
Volume 6 Issue No. 6
A Review on Various Image and Video Restoration Techniques
Er. Manisha Sharma1, Er. Kiran Gupta2
Department of Computer Science Engineering
Swami Devi Dyal Institute of Engineering and Technology, Golpura, Barwala, Panchkula, India
[email protected], [email protected] 2
Abstract:
Reconstruction of low value deteriorated image into high quality improved image is termed as image restoration. The sight of this
paper is to have knowledge about mixed restoration techniques like Average filter, Median filter, Wiener filter, Blind deconvolution, and wavelet transform etc. The basis of restoration is to undo the operation of degraded image. There are many
grounds because of which degradation takes place like poor weather conditions, camera mis-focus, motion blur noise i.e.
Gaussian noise, salt and pepper noise, speckle noise, Poisson noise etc. The idea behind this paper is to bind various restoration
techniques in order to have de-blurred, high value, multi-resolution image/video and to recover the original image with minimum
loss of precision. Degradation model and review of many restoration approaches to form an actual image qualitatively and
quantitatively has been explored in this paper. A quick comparison of various techniques including their advantages and
disadvantages are highlighted to have an easy view of various techniques and to mind the importance of restoration in any field.
Keywords: Average filter, DCT, Median filter, PSF, Wavelet Transform, Wiener filter.
I.
INTRODUCTION
An image is an uninterrupted two dimensional delivery of
luminance or some other visible effect and video is
concatenation of images to shape a paradigm. From early
period to modern time images have been used to figure out or
to stand for something. With the advent of modern century
videos came into lifestyle. There are numerous applications
where high value image and video has its importance like
astronomical imaging, magnetic resonance imaging, visual
perception, architecture, military applications etc.
Quality of anything has to be preserved at any cost for its
proper functioning. This paper figures out retrieval of a
corrupted image or video and this process of recuperate an
image from its worst ground is called as restoration.
Restoration deals with picture rebuilding with minimum loss
of data.
The major concern is to refine an image or sequence of images
from its contorted stage. There are two types of data i.e. useful
data and unwanted data. This unwanted data can be any
hurdle in making a high quality image or video like any kind
of noise, environmental conditions etc.
The remainder of this paper is organized as follows: Section II
describes the fundamental techniques for restoration of the
imaging system. Section III explains the comparison of
various filters that are used in restoring a contorted image or
video and their results. Section IV ends up with conclusion of
this paper.
degradation. Additive noise which is merged with degradation
function can be [5] Poisson noise, salt and pepper noise,
Gaussian noise etc. whose probability density function has
been shown in figure 2. (a),(b),(c). The image that is destined
from source is the restored image which is refined by various
restoration filters whose comprehensive illustration has been
weighed up in next section.
Fig 1: Block Diagram of Degradation/Restoration model
This is how the degradation and restoration process works.
Generally, contortion of an image/video occurs during
acquisition and transmission phase.
IMAGE DEGRADATION/ RESTORATION PROCESS
This figure 1. shows that degradation function and additive
noise both are combined and operates on an input image f(x,y)
to outlet a degraded image f’(x,y). Some restoration filters are
applied on g(x,y) to have an original restored image f’(x,y). A
systematic image/video can be degraded in view of the fact
that an act occurred which is unenviable or due to defective
communication channel. Bad weather conditions and
indecorous camera arrangement can be an addition to
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Fig 2: (a) PDF of Gaussian noise [5]
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Fig 2: (b) PDF of Poisson noise [5]
Average filters are easy to use and simple in functioning but it
produces skewed results [5]. Average filter has its application
in image and signal processing. Because of its simple design
utilisation this filter has wide scope but it disappoints with
skewed results.
b) Median Filter
The median filter is a category of non-linear digital filtering
technique [2] that is helpful to abandon impulsive noise from
data. Non-linear filters are those filters whose result never a
linear function according to its input. The filter takes into
consideration each and every pixel in the neighbourhood that
comes its way and then substitute them with the median value.
All values are examined statistically. Now, to calculate the
median value [2] all the pixel values are arranged in increasing
order from the neighbourhood values and then the pixel that is
to be examined is replaced with the median value.
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Fig 2: (c) PDF of salt and pepper [5]
II. RESTORATION TECHNIQUES
Restoration of contorted image/video is formulated as
regression problem [1]. To revert back close to original
image/video without violating its quality is done by restoration
techniques and to nourish the smoothness of that particular
image/video that has been taken for examination.
It deals with two types of techniques [2]:
1.
Spatial Domain Techniques
2.
Frequency Domain Techniques
Spatial Domain Techniques:
In this type of filtering, image or video is recovered directly
i.e. operation is applied on the pixels of an image without
undergoing any path. Spatial Filtering is considered only
where additive noise exists. It is a very conventional approach.
Restoration with spatial filters
a) Average Filters
This filter has its synonym with mean filter. It is a category of
low pass frequency filter [5]. Low pass filters are those filters
that proceeds signals with lesser frequency than a particular
threshold frequency.
As its name implicates, it wields by averaging a
number of pixels from source image to yield each pixel in the
destination image. It is usually considered as a convolution
filter. Similar to other convolutions, it is also deployed around
a kernel and usually 3x3 matrix is taken into consideration.
Here, in this equation, {X(. , .)} and {Y(. , .)} are the inputs
and outputs. ‘W’ is the window that is considered for
coordinating the neighbourhoods of origin.
Fig 4: (a) Image corrupted by Gaussian noise (b) Filtered
image after passing through Median filter [2]
Median filters are next version of mean filters and produce
enhanced results but they are relatively expensive and tangled
to evaluate [2] [5].
c) Wiener Filter
The wiener filter acts as superlative exchange [4] between
inverse filtering and smoothing of noisy data. Inverse filtering
is also a fact of low pass filter, then that image/video can be
reverted back by generalized inverse filtering. This is the
beneficial technique for erasing the additive noise in degraded
data and at the same time inverts blurring effect as well.
Weiner filter implements pixel wise adaptive filtering of any
image/video i.e taken under examination.
Fig 5: Principle of Wiener Filter
Fig 3: (a) original image (b) image filtered with Gaussian
noise and salt & pepper noise (c) filtered image with average
filter [5]
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This figure [4] shows the connection between input and
output. Here, input is a random signal where s(n) is that signal
which doesn’t have any noise, v(n) is the signal that contains
noise. The ideal output y(n) is termed as the estimate value of
s(n) which is presented as s^(n).
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Wiener filter operates on these situation where degradation
function is zero but this filter leaves residual noise afterwards.
Wiener filter has its application in digital communication,
channel equalization and noise reduction. The objective of
wiener filter is to have a metric where mean squared error can
be minimised as much as possible.
DCT has its accessory property of getting lapped. Its design is
to perform on consecutive blocks of dataset.
Now, suppose an image which is has its
dimensionality as NXM, where f (i , j) is pixel intensity in ith
row and jth column.
Then, DCT coefficient, F(u,v) will be :
This method has a remarkable feature of performing
mathematical computations within lesser period of time and
because of this it is used in scope but it doesn’t reflects on
binary
images.
Fig 6: (a) original image (b) noisy image (c) restored with wiener
filter [4]
Frequency Domain Techniques
Direct filtering is not applied in this technique. Filtering
process reaches the goal by mapping the spatial domain into
frequency domain with the help of Fourier transform of image
function. After the completion of filtering process, image is
again mapped into spatial domain by reverse method i.e.
inverse Fourier transform to get restored image.
Restoration with Frequency Domain Filters
a) Wavelet Transform
Wavelet Transform is a mathematical function that helps to
split any given function into various scale constituents. An
important advantage this transform has is that it apprehends
both frequency and location information [4]. It has it’s
pliability in multiscale solution and to reconstruct high value
images/videos. This technique disintegrates the low level
frequency components whereas the present level detailed
components remains intact [1] [14].
As already described wavelet transform has its function to
separate out the signals into their coefficients and compare the
recurrence groups.
In two dimensional images every level is partitioned into four
sub bands LL, LH, HL, HH and here L and H are low and high
frequency band. These bands are further separated into LH1
HL1 ,HH1 that are termed as wavelet coefficients as shown in
figure 7.
Fig 8: a) Image corrupted by Gaussian noise b) Filtered image
after Discrete Cosine Transform [12]
c) Blind Deconvolution
This is the process [15] that evaluates both real image and
blurred image from contorted image but by using partial
information about that imaging system. Deconvolution is an
approach that attempts to invert the degradation of imaging
system that was pre modelled by convolution. It has its
applications in astronomical speckle imaging [15], remote
sensing, medical imaging etc. This method doesn’t need any
prior knowledge on kernel but other techniques require user
interactions in order to produce some precise information of
contorted image/video.
This figure represents the blind deconvolution architecture that
requires an original image with clear quality as input. This
input image will be passed through degradation model about
which we have discussed already that will produce a blurred
image with low quality. In order to enhance its appearance
blurred image will be passed through blind deconvolution
algorithm whose main aim is to extravagate the quality of
blurred image/video and we reach the destination with
deburred image/video as output.
Fig 7: Wavelet Transform [4]
b) Discrete Cosine Transform (DCT)
This filter has been proposed in 1974. The main function DCT
performs is to reduce the dimensionality of image/video and
used for correlated noise. This method labours appropriate for
salt and pepper noise [2]. Its transform domain features can
easily be gained by zonal filtering and zonal coding. Zonal
coding is the process [2] where zonal mask is exercised to the
transformed blocks and only those blocks are encoded that are
nonzero. It is necessary to compress images before getting
transmitted.
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Fig 9: Blind Deconvolution Architecture
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Blind deconvolution method can be explored both iteratively
and non-iteratively. In the former approach every iteration
have some estimation of point spread function (PSF) and by
this knowledge of PSF we can enhance the resultant
image/video frequently by putting this value neat to the real
image.
In the latter i.e. non-iterative approach, any one application
that has some exterior information brings out the PSF value
and this value will be needed further to restore the contorted
image/video from the real one.
Fig 11: a) Original Image b) Image with Gaussian noise c)
Image with salt and pepper noise. [5]
Table 1: Performance Comparison for Gaussian Noise
S.
No.
Fig 10: (a) Blurring with oversized PSF (b) Filtered after blind
deconvolution[12]
III. RESULTS AND DISCUSSION
As we have already explored that there are two
fundamental techniques for enhancing the perfection of
contorted image or video i.e. Spatial Domain and Frequency
Domain techniques.
Two parameters are always taken into consideration for
comparing the performance of filtering techniques:1) Peak Signal to noise ratio (PSNR)
2) Mean Squared Error (MSE)
PSNR is widely used for improving the quality of contorted
and distorted image or sequence of images [9]. It measures the
peak error. For instance, a picture with 8 bits for each pixel
contains numbers from 0 to 255. The PSNR is generally
utilized as measure of value reconstruction of picture. This is
the ratio of maximum power and power signal noise. The
more the signal strength the lesser the data becomes noisy.
PSNR can be calculated as [3]
Metrics
Average
Filter
Median
Filter
Wiener
Filter
1.
PSNR
33.2805
33.0336
34.2525
2.
MSE
5.4056
5.5658
4.8229
Table 2: Performance Comparison for Salt and Pepper Noise
S.
No.
Metrics
Average
Filter
Median
Filter
Wiener
Filter
1.
PSNR
32.7332
37.5965
34.0572
2.
MSE
4.9548
3.2668
5.7428
As it can be seen from above two tables that the PSNR value
of median filter is more when salt and pepper noise is
introduced but on the other side i.e. Gaussian noise its value is
lesser than Wiener filter. So, it is evident that median filter
works according to the signal noise but Wiener filter has the
ability to reduce MSE value as much as possible.
Table 3: Performance Comparison for Gaussian Noise of
Frequency Domain Techniques
MSE measures the average of the square of errors i.e. the
difference between the estimator value and the value what is
estimated. The MSE is the second snippet of mistake and in
this way consolidates both the difference of the appraisal and
its inclination. MSE is a risk capacity, comparing to the
normal estimation of the squared mistake misfortune or
quadratic misfortune. MSE occurs due to structured entropy.
As the PSNR increases MSE decreases along with it because
they both are inversely proportional to each other.
MSE can be calculated as [3]
The image that is depicted in Figure 11 (a) is taken as input
image and Gaussian noise salt and pepper noise is introduced
on it as shown in Figure 11 (b) (c). After applying the noise,
these two metrics are evaluated on various filters in order to
have a restored image which is depicted in Tables.
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S.
No.
Metrics
Wavelet
Transform
Discrete
Cosine
Transform
Blind
Deconvolution
PSNR
21.4737
20.6789
34.0179
MSE
463.1379
492.5637
25.7804
1.
2.
With the help of evident results wavelet transform is better
than discrete cosine transform in terms of efficiency and
quality but DCT takes less computation time than other two
techniques [9]. Blind deconvolution produces better results as
compared with other frequency domain techniques as its
PSNR value is higher.
The objective of the rebuilding methodology is to enhance the
given picture with the goal that it is appropriate for further
preparing.
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Table 4: Comparison of Various Techniques
S. No.
RESTORATION
METHODS
1.
AVERAGE FILTER
2.
MEDIAN FILTER
3.
.
WIENER FILTER
4.
.
WAVELET TRANSFORM
5.
DISCRETE
TRANSFORM
COSINE
6.
BLIND DECONVOLUTION
ADVANTAGES
DISADVANTAGES
APPLICATIONS
Easy to use.
Simple in Design.
Skewed results
produced.
Image processing.
Image segmentation.
Efficient with high
filter mask.
Better results.
Controls
output
error.
Straightforward to
design.
Good localization
in
time
and
frequency domain.
Efficient results.
complex to implement
Poor results with low
size filter mask.
Leaves residual noise.
Slow to apply.
Less Computation
time.
Not
efficient
binary images.
Better results.
Efficient.
Robust to noise.
Complex.
PSF can loss.
IV. CONCLUSION
This paper presents a review of various image and video
restoration techniques. Restoration is the process that can
occur during image acquisition and transmission phase. So,
it’s a vital move in image processing. There are various
methods in spatial and frequency domain to nurture the
imaging system. Both techniques are mixed together to
improve the overall contrast of the entire image. Although
restoring an imaging system is a tedious task but this paper
attempts to generalize various methods that can be beneficial
in enhancing the outlook of that imaging system i.e. with the
help of spatial and frequency domain techniques. This paper
has also discussed the estimation parameters which is PSNR
and MSE. Both these metrics are inversely proportional to
each other. Among spatial techniques wiener filter gives
accurate results and in frequency domain techniques blind
deconvolution has its importance. All the techniques have
their own advantages and disadvantages which can be helpful
in designing any novel filter for future betterment.
V.
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Er. Kiran Gupta is born on 1 April
1985. She completed B.tech (Computer Science
Engineering) from Swami Devi Dyal Institute of
Engineering and Technology, Golpura, Barwala,
Panchkula,Haryana, India in 2007 and M.tech
(Computer Science Engineering) from DCSA,
Kurukshetra, Haryana, India in 2010. Her areas of
interest are Digital Image Processing, Wireless Sensor
Networks and Data Mining.
Er. Kiran Gupta has 5 years of teaching experience.
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Author Details
Er. Manisha Sharma is from Nangal. Born on
7 February 1993. She completed B.tech (Computer Science
and Engineering) from Maharishi Markandeshwar University,
Mullana, India in 2014.
She is pursuing M.tech (Computer Science and Engineering)
from Swami Devi Dyal Institute of Engineering and
Technology, Golpura , Barwala, Panchkula, Haryana, India.
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