Restoration of Blurred Images Using Blind Deconvolution AlgorithmMs.S.Ramya Kalasalingam University, Anand Nagar, Krishnankoil Ms.T.Mercy Christial Kalasalingam University, Anand Nagar, KrishnankoilAbstract: Image restoration is the process of recovering the original image from the degraded image. Aspire of the project is to restore the blurred/degraded images using Blind Deconvolution algorithm. The fundamental task of Image deblurring is to de-convolute the degraded image with the PSF that exactly describe the distortion. Firstly, the original image is degraded using the Degradation Model. It can be done by Gaussian filter which is a low-pass filter used to blur an image. In the edges of the blurred image, the ringing effect can be detected using Canny Edge Detection method and then it can be removed before restoration process. Blind Deconvolution algorithm is applied to the blurred image. It is possible to renovate the original image without having specific knowledge of degradation filter, additive noise and PSF. To get the effective results1, the Penalized Maximum Likelihood (PML) Estimation Technique is used with our proposed Blind Deconvolution Algorithm.Key words： Blind Deconvolution Algorithm; Canny Edge Detection; Degradation Model; Image restoration; PML; PSF1 IntroductionImage deblurring is an inverse problem which whose aspire is to recover an image which has suffered from linear degradation. The blurring degradation can be spaceinvariant or space-in variant. Image deblurring methods can be divided into two classes: nonblind, in which the blurring operator is known. And blind, in which the blurring operator is unknown2.Blurring is a form of bandwidth reduction of the image due to imperfect image formation process. It can be caused by relative motion between camera and original image.Normally, an image can be degraded using low-pass filters and its noise. This low-pass filter is used to blur/smooth the image using certain functions.Image restoration is to improve the quality of the degraded image. It is used to recover an image from distortions to its original image. It is an objective process which removes the effects of sensing environment. It is the process of recovering the original scene image from a degraded or observed image using knowledge about its nature. There are two broad categories of image restoration concept such as Image Deconvolution and Blind Image Deconvolution .Image Deconvolution is a linear image restoration problem where the parameters of the true image are estimated using the observed or degraded image and a known PSF (Point Spread Function). Blind Image Deconvolution is a more difficult image restoration where image recovery is performed with little or no prior knowledge of the degrading PSF. The advantages of Deconvolution are higher resolution and better quality.This paper is structured as follows: Section 2 describes the degradation model for blurring an image. Section 3 represents Canny Edge Detection. Section 4 describes the deblurring algorithm and overall architecture of this paper. Section 5 describes the sample results for deblurred images using our proposed algorithm. Section 6 describes the conclusion, comparison and future work.2 Degradation ModelIn degradation model, the image is blurred using filters and additive noise. Image can be degraded using Gaussian Filter and Gaussian Noise. Gaussian Filter represents the PSF which is a blurring function. The degraded image can be described by the following equation (1) （equation 1）In equation (1), g is degraded/blurred image, H is space invariant function (i.e.) blurring function3, f is an original image, and n is additive noise. The following Fig.1 represents the structure of degradation model. Fig.1 Degradation ModelImage deblurring can be done by the technique, Gaussian Blur. It is the convolution of the image with 2-D Gaussian function.A) Gaussian Filter:Gaussian filter is used to blur an image using Gaussian function. It requires two parameters such as mean and variance. It is weighted blurring. Gaussian function is of the following form (equation 2)where is variance and x and y are the distance from the origin in the horizontal axis and vertical axis Gaussian Filter has an efficient implementation of that allows it to create a very blurry blur image in a relatively short time.B) Gaussian Noise:The ability to simulate the behavior and effects of noise is central to image restoration. Gaussian noise is a white noise with constant mean and variance. The default values of mean and variance are 0 and 0.01 respectively.C) Blurring Parameter:The parameters needed for blurring an image are PSF, Blur length, Blur angle and type of noise. Point Spread Function is a blurring function. When the intensity of the observed point image is spread over several pixels, this is known as PSF. B