Image Restoration Research Paper

© iCARE - International Journal on Concurrent Applied Research in Engineering and Management ISSN
(
P
)
:2395-2474
ISSN
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O
)
:2395-7425
A SURVEY ON IMAGE RESTORATION USING
BLIND DECONVOLUTION
1
1
N.YUVARAJ
Research Scholar, Department of Computer Science and Engineering,
.
1
Sathyabama University, Chennai, India.
1
[e-mail]
Abstract: Image restoration is the operation of taking a corrupted/noisy image and estimating the clean original image. Corruption may come in many forms such as motion blur, noise, and camera misfocus.
Deconvolution is an example of image restoration method. It is capable of increasing resolution, especially in the axial direction, restoring the original image from blurred or degraded image due to motion blur,

Deconvolution can be used to get an image nearer to the real object. In this paper we presented the analysis of blind deconvolution and survey of some of the published work in this area since 1990 to till date. This paper presents a time-line view of the advances made in this field, Image Restoration using blind deconvolution to carry out a comparative study to evaluate the performance of various image restoration algorithms.
Keywords : Blind Image Deconvolution, Image Restoration, Point Spread Function, Gaussian Noise
1. INTRODUCTION
The Classical image restoration aims at recovering an estimate of an original image, assuming the degradation completely known. But these assumptions are not always satisfied. In most of the situations, it is often costly or physically impossible to obtain the prior information about the degradation process. This motivates and direct us to blind image restoration, where the primary goal is to recover an estimate of the original image given little or no prior knowledge about the degradation process. This is a difficult and ill-posed problem as the uniqueness and stability of the solution is not guaranteed. The Blind image deconvolution is

The blurring is mathematically modeled as a convolution. The process of inverting this operation is termed deconvolution. There are situations where explicit knowledge of the convolution kernel cannot be assumed. Therefore, inverting the convolution operation under those circumstances is termed as blind deconvolution.
2. RELATED WORK
The main objective of Image Restoration is to recover the original image from a degraded image which is blurred by a degradation function, commonly by a Point Spread Function
(
PSF
)
.
Image Restoration Techniques [2] are divided into two categories on the basis of knowledge about
Point Spread Function
(
PSF
)
.
2.1 Blind Image Restoration
In this, the reconstruction of original images from degraded images even when we have very little or no knowledge about PSF.
Blind Image Deconvolution
(
BID
)
is an algorithm of this type.
2.2 Non-Blind Restoration
In this, the reconstruction of original images from degraded images when we know that how image was degraded i.e. We have a knowledge about PSF. Deconvolution using Lucy
Richardson Algorithm
(
DLR
)
, Deconvolution using Weiner Filter
(
DWF
)
, Deconvolution using
Regularized Filter
(
DRF