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COLORED IMAGE RESTORATION USING
NEIGHBORING PIXEL METHOD
Nazia Hossain
Department of CSE
Military Institute of Science
and Technology, Dhaka, Bangladesh
[email protected]
Amdadul Haque
Department of CSE
Military Institute of Science
and Technology, Dhaka, Bangladesh
[email protected]
Naresh Sing Chauhan
Department of CSE
Military Institute of Science
and Technology, Dhaka, Bangladesh
[email protected]
Abstract—The field of image processing is evolving rapidly.
During the recent years, there has been a significant improvement
in the level of interest in image processing algorithm for
image restoration, image registration, image morphology, neural
networks, full-color image processing, image data compression,
image recognition and knowledge-based image analysis systems.
An image can be corrupted by various kind of motion, noise or
distorted signal during the process of acquisition and to detect
the reason for distorting an image various method of restoration
has been introduced in image processing. To restore a corrupted
image there are number of methods have been introduced in
this eld including neighboring pixel method. Under this context,
an effort has been given to restore a corrupted image with the
neighborhood pixel method using mean value. We calculated the
mean values of neighbor pixel and replaces the mean values
with the target pixel of input image. We investigated the noise
level of RGB and grayscale images and improved the noise level
using neighboring pixel method that enhances the target image
comparing with existing noise reduction filters.
Index Terms—Image processing, Neighboring Pixel, Image
Restoration, Noise Reduction
I. INTRODUCTION
In imaging science and computer vision, image processing
is a technical analysis of the complex aspects of an image,
deploying algorithms in which the input is an image, such as
a photograph or video frame; the output of image processing
may be either an image or a set of characteristics or parameters
related to the image. Worldwide the eld of image processing
is growing rapidly.
An image is a visual representation of something. In information
technology, the term has several usages:
i. An image is a picture that has been created or copied and
stored in electronic form. An image can be described in terms
of vector graphics or raster graphics.
ii. An image is a section of random access memory ( RAM
) that has been copied to another memory or storage location.
A digital image is a representation of a two-dimensional
image as a finite set of digital values, called picture elements
or pixels. Pixel values typically represent gray levels, colors,
heights, opacities etc. Digitization implies that a digital image
is an approximation of a real scene.
Pixel is the basic unit of a digital image which is also known
as picture element. Pixel describes programmable color on a
computer display of each point of an image.In digital imaging,
a pixel, or picture element is a physical point in a raster
image, or the smallest addressable element in an all points
addressable display device.In color image systems, a color is
typically represented by three component intensities such as
red, green, and blue.
An image can be corrupted by various kind of motion, noise
or signal during the process of acquisition. To detect this motion
or noise in the picture various method of restoration has
been introduced in image processing 7. In general, the noise
in the image is defined by the regions which are remarkably
different i.e. darker or shiner comparing with the pixel with
the neighboring pixels. To overcome this problem author rstly,
focused on the causes of blur or corrupted image, among
various method of restoration, emphasizes the neighboring
pixel method, developed an algorithm for the restoration of
an image by using distance transformation and mean method.
Image processing is a method which includes some operations
applying in any image in order to get an enhanced image
or to extract some useful information from it. Image processing
7 refers to the analysis and manipulation of a digitized image,
especially in order to improve its quality.
Digital Image is a two-dimensional function x and y are
spatial coordinates. The amplitude of f(x,y) is called intensity
or gray level at the point (x,y).
Image processing refers to the analysis and manipulation of
a digitized image, especially in order to improve its quality.
The Author emphasizes on solving identification problems
primarily noticed in forensic medicine, or in the creation of
weather maps from satellite images. The bitmapped graphics
format images that have been scanned in or shot with digital
cameras – which is used to deal with the reconstruction or
enhancement of the uncorrupted image from a noisy one.
A good number of filtering schemes such as Wiener filter4,
Bilateral filter 12, Bayseian based iterative method 10 are
developed over time for noise reduction and image restoration.
The Median filter 2 is one the filtering schemes that are
widely used for noise reduction and also have applications in
digital signal processing.
The main objective of this paper is to introduce an approach
for image restoration, whose main aim is to make an image
noise-free. The entire paper is organized in the following sequence.
In section-1, Literature review about image restoration
has been proposed. In section-2, the nearest neighbour method
and mean method including algorithm has been introduced.
In section-3, the result obtained for the implementation of
algorithm in MATLAB has been presented with analysis.
Finally, the paper concludes with conclusion and references.
II. RELATED WORK
Images can be corrupted for various reasons such as motion
blur, noise, and camera misfocus. To improve the quality of a
degraded image, restoration has to be done. Image restoration
is the task of minimizing the degradation in an image i.e. to remove
the noise or motion blur. Image restoration assures good
insights of image when it is subjected to further techniques of
image processing7.
Image restoration refers to remove motion or distortion from
image and get an improved quality of that defected image.
Images are often degraded during the data acquisition process.
The degradation may involve blurring,information loss due to
sampling, quantization effects, and various sources of noise.
Therefore, the purpose of image restoration is to estimate the
original image from the degraded data. Applications of image
restoration5. are needed in various sector like from medical
imaging, astronomical imaging,to forensic science, etc. Often
the benefits of improving image quality to the maximum
possible extent far out weight the cost and complexity of the
restoration algorithms involved 1.
Lee and Jong-Sen6 described Computational techniques
on 2D image arrays that are developed on the basis of
images’ local mean and variance to contrast enhancement and
noise filtering. They rely on nor-recursive algorithms and did
not use any kind of image transform. The proposed method
is obvious in real-time image processing applications with
parallel processing.
An algorithm in MATLAB which is based on the neighborhood
property of a pixel. We focus on a certain iterative
process to carry out restoration. One such method described in
this regard is the Mr. JAGADISH H. PUJAR and Mr.KIRAN
S. KUNNUR proposed an image restoration Nearest Neighbourhood
Method 5.
E.P. Simoncelli, E.H. Adelson11 introduces a method
which is a classical solution to the noise removal problem is
the Wiener filter, which utilizes the second-order statistics of
the Fourier decomposition. Authors developed an extension
of the Wiener solution which is a Bayesian estimator that
performs a “coring” operation, and a simple model of the
subband statistics to develop a semi-blind noise removal
algorithm which relies on a steerable wavelet pyramid.
K. Miyata, N. Tsumura, H Haneishi and Y Miyake 8
proposed a Wiener filtering method that can improve the
total quality of images corrupted by additive noise without
degrading the sharpness caused by the noise reduction process.
They observed covariance matrices of the images are estimated
from the neighboring pixels which are selected around the
current pixel with a color classification technique.
Nowak, Robert D proposed a novel wavelet-domain noise
reduction procedure that is diversified to variations in signal
and to the noise. It is believed that magnetic resonance and
magnitude image data follows a Rician distribution 9. The
Rician noise is troublesome in low Signal to Noise Ratio
(SNR) domain to which it creates random fluctuations but
reduces noise. In that work, authors studied and proposed a
method for Rician noise removal.
Statistical data analysis also helps to remove noise from
target images. Garnett, Roman and Huegerich, Timothy and
Chui, Charles and He, Wenjie introduced image statistic that
identifies noise pixels with impulse noise of random values of
image pixels 3. They quantify intensity differentiation from
neighbor pixels, demonstrated how proposed image statistic
can be introduced to remove additive Gaussian noise and
capable to reduce both Gaussian and impulse noises effectively
from the noisy images.
III. PROPOSED WORK
Digital Image processing stems from various tasks of practical
techniques such as Classification, Feature extraction,
Multi-scale signal analysis, Pattern recognition and Projection
and these tasks are almost impossible to solve efficiently using
analog apparatus methods. To restore noisy image many filters
have been proposed like Guided filter, Gaussian filter, Wiener
filter and Median filters are some of the restoration techniques.
We have deviced a new approach to restore a RGB image using
Neighboring Piexl with mean method.
A noisy RGB image was taken as input and calculated its
noise in MATLAB. Then we have taken the nearest neighbor
pixels, 8 for 3×3 matrix and extracted a submatrix of each
pixel. Calculate the mean of submatrix for each pixel. Then replace
the center pixel with the mean of its neighboring pixels.
Using an iterative process each pixel has been considered and
traversed. After the whole traversal, we have got our desired
output image.Then we again calculate the noise of restored
image and compare it with previous noise.
We developed a prototype using Matlab. The entire process
has been represented in Fig. 1. Sample images are collected
from Wikipedia images.
A. Image Acquisition
There are some fundamental steps of image processing. Image
Acquisition is the first step or process of the fundamental
steps of digital image processing. Image acquisition could be
as simple as being given an image that is already in digital
form. If not we can easily convert it to digital form. Generally,
the image acquisition stage involves preprocessing. In our
method we take a noisy RGB image as input for restoration
and calculated its noise in MATLAB.

element m¯ j is the mean value of the N observations of the
J
th variable:
m¯ j =
1
N
X
N
i=1
mi,j j = 1, …, K (1)
Thus, the sample mean vector contains the average of the
observations for each variable, and is written as:
m¯ =
1
N
X
N
i=1
mi =
?
?????????????
m¯ 1
.
.
.
m¯ j
.
.
.
m¯ K
?
?????????????
(2)
E. Pixel Replacement
Above mentioned, to carry out restoration, we consider
the mean of nearest neighbours of a pixel. In our approach,
we consider a 3×3 matrix, total of eight neighbours of each
pixel.The size of the window can be more than 3×3 too. In the
2D grid of picture, each pixel has a certain correlation with
its nearest pixels. With the aid of this property, we introduce a
method to replace a noisy pixel by a value which happens to
be the mean of all the nearest neighbors in a filtering window
of 3×3. This ensures a good level of noise reduction from
image as shown in the results.
IV. RESULT ANALYSIS
Image Restoration is the operation of taking a corrupted or
noisy image and estimating the clean, original image in which
the input is an image, such as a photograph or video frame;
the output of image processing may be either an image or a
set of characteristics or parameters related to the image. Our
approach was applied on both a noisy RGB image and a noisy
grayscale images and obtained much better result comparing
other noise removal schemes.
Fig. 4: Noisy Image vs Restored Image.
In Fig-4, the input image is a noisy image which has some
degree of noise density in it and the restored image each
corrupt pixel is replaced by the mean value of its neighbours.
We tested on several standard test images and the above result
was obtained for the image which is corrupted by noise. As
a result of this, the above result was obtained for mean of
neighboring pixel i.e. for a given pixel at (i,j), all the eight
neighbours of it are taken into account for restoring a pixel at
(i,j).
Fig. 5: Noise after applying different filters for RGB image
The bar graphs comparison of resulted noise for an RGB
image after applying different image restoration techniques
i.e. Guided filter, Gaussian filter. It can be clearly seen
that the amount of noise reduced by proposed method is
significantly high. In original image, amount of noise calculated
approximately 4×10?3 using available noise estimation
routine. Guided filter, Gaussian filter remove the noise at
approximately 2×10?3
and proposed method reduce the noise
significantly to 1×10?3
, that is, proposed method reduces noise
by 79% of original noise.
Restoration
Method
Image
Format
Original
Noise
Resulted
Noise
Reduction
Rate
Guided filter RGB 3.9×10?3 2.3×10?3 41%
Gaussian filter RGB 3.9×10?3 1.9×10?3 51%
Wiener filter RGB N/A N/A N/A
Median filter RGB N/A N/A N/A
Proposed
Method*
RGB 3.9×10?3 0.8×10?3 79%
TABLE I: Noise level for different Filters using RGB image.
We could not calculate noise level of Wiener filter and
Median filter as mentioned in table by N/A.
Fig. 6: Noise after applying different filters for grayscale image
We experimented different filters like Guided filter, Gaussian
filter, Wiener filter, Median filters for grayscale images as
well and proposed method outperformed the mentioned noise
removal techniques. Initial noise level observed in original
image is approximately 0.03 and mentioned filters the remove
noise level whereas best output obtained by Median filter.
However, the proposed method reduces the noise level to
0.004. The result obtained is shown in both Table-1 and Fig.
6, that is proposed method reduces 85% of the noise from
original image.
Restoration
Method
Image
Format
Original
Noise
Resulted
Noise
Reduction
Rate
Guided filter RGB 0.027 0.017 37%
Gaussian filter RGB 0.027 0.013 51%
Wiener filter RGB 0.027 0.008 70%
Median filter RGB 0.027 0.006 77%
Proposed
Method*
RGB 0.027 0.004 85%
TABLE II: Noise level for different Filters using Grayscale
image.
V. CONCLUSION AND FUTURE WORK
In this work, we mainly focused nearest neighborhood pixel
and mean method. The algorithm combining these methods are
able to restore the image to a significant level. We take a sub
matrix of 3/3 and calculate the mean of the sub matrix. Then
replace the center pixel with the mean of neighboring pixels.
The restored image obtained after performing these steps are
of much better.
However, using proposed method pixels at the boundary
cannot be considered as the center pixels. As a result, after
applying the proposed technique the given image holds noise
or blur at the edges. Therefore, boundaries noise cannot be
reduced as desired an in future that could be focused on.
For the further enhancement of image, various feature
detection method like edge detection methods like sobel edge
detection technique or various filtering can be applied and used
on the basis of efficiency to remove the noise completely at
the edges and boundaries of the pictures.
REFERENCES
1 Mark R Banham and Aggelos K Katsaggelos. “Digital
image restoration”. In: IEEE signal processing magazine
14.2 (1997), pp. 24–41.
2 Tao Chen, Kai-Kuang Ma, and Li-Hui Chen. “Tri-state
median filter for image denoising”. In: IEEE Transactions
on Image processing 8.12 (1999), pp. 1834–1838.
3 Roman Garnett et al. “A universal noise removal algorithm
with an impulse detector”. In: IEEE Transactions
on image processing 14.11 (2005), pp. 1747–1754.
4 Allen D Hillery and Roland T Chin. “Iterative Wiener
filters for image restoration”. In: IEEE Transactions on
Signal Processing 39.8 (1991), pp. 1892–1899.
5 H PUJAR JAGADISH et al. “A novel approach for
image restoration via nearest neighbour method”. In:
Journal of Theoretical and Applied Information Technology
14.2 (2010).
6 Jong-Sen Lee. “Digital image enhancement and noise
filtering by use of local statistics”. In: IEEE transactions
on pattern analysis and machine intelligence 2 (1980),
pp. 165–168.
7 Robert M Lougheed. Neighborhood image processing
stage for implementing filtering operations. US Patent
4,541,116. Sept. 1985.
8 Kimiyoshi Miyata et al. “Restoration of Noisy Images
using Wiener Filters Designed in Color Space”. In: IS
AND TS PICS CONFERENCE. SOCIETY FOR IMAGING
SCIENCE & TECHNOLOGY. 2000, pp. 301–306.
9 Robert D Nowak. “Wavelet-based Rician noise removal
for magnetic resonance imaging”. In: IEEE Transactions
on Image Processing 8.10 (1999), pp. 1408–1419.
10 William Hadley Richardson. “Bayesian-based iterative
method of image restoration”. In: JOSA 62.1 (1972),
pp. 55–59.
11 Eero P Simoncelli and Edward H Adelson. “Noise
removal via Bayesian wavelet coring”. In: Image Processing,
1996. Proceedings., International Conference
on. Vol. 1. IEEE. 1996, pp. 379–382.
12 Buyue Zhang and Jan P Allebach. “Adaptive bilateral
filter for sharpness enhancement and noise removal”.
In: IEEE transactions on Image Processing 17.5 (2008),
pp. 664–678.

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