圖像處理外文翻譯---模糊圖像的盲反卷積復(fù)原算法（英文）

1、Restoration of blurred images using Blind Deconvolution AlgorithmMs.S.Ramya Kalasalingam University, Anand Nagar, Krishnankoil ramyareys@gmail.com Ms.T.Mercy Christial Kalasalingam University, Anand Nagar, Krishnankoi

2、l Abstract 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 funda

3、mental 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

4、 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.

5、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 resul

6、ts, the Penalized Maximum Likelihood (PML) Estimation Technique is used with our proposed Blind Deconvolution Algorithm. Keywords: Blind Deconvolution Algorithm, Canny Edge Detection, Degradation Model, Image restorat

7、ion, PML, PSF I. INTRODUCTION Image deblurring is an inverse problem which whose aspire is to recover an image which has suffered from linear degradation. The blurring degradation can be space- invariant or space-in v

8、ariant. 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 unknown. Blurring is a form of bandwidth reduction of

9、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

10、 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

11、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

12、 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

13、 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. Th

14、e 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.

15、 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, comp

16、arison and future work. II. DEGRADATION MODEL In 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

17、 PSF which is a blurring function. The degraded image can be described by the following equation (1) In equation (1), g is degraded/blurred image, H is space invariant function (i.e.) blurring function, f is an origina

18、l image, and n is additive noise. The following Fig.1 represents the structure of degradation model. Fig. 1 Degradation Model PROCEEDINGS OF ICETECT 2011978-1-4244-7926-9/11/$26.00 ©2011 IEEE 496The original image

19、 is degraded or blurred using degradation model to produce the blurred image. The blurred image should be an input to the Deblurring algorithm. Various algorithms are available for deblurring. In this paper, we are g

20、oing to use Blind Deconvolution Algorithm. The result of this algorithm produces the deblurring image which can be compared with our original image. Fig. 2 Overall Architecture A) Blind Deconvolution Algorithm: Blind

21、 Deconvolution Algorithm can be used effectively when no information of distortion is known. It restores image and PSF simultaneously. This algorithm can be achieved based on Maximum Likelihood Estimation (MLE). 1)

22、Algorithm for Deblurring: Input: Blurred image ‘g’ Initialize number of iterations ‘i’ Initial PSF ‘h’ Weight of an image ‘w’ % pixels considere

23、d for restoration a=0 (default) %Array corresponding to additive noise Procedure – II If PSF is not known then Guess initial value of PSF Else Specify th

24、e PSF of degraded image Restored Image f’= Deconvolution (g, h, i, w, a) End Procedure – II V. SAMPLE RESULTS The below images represent the result of degradation model using Gaussian blur. First image represented th

25、e original image and its edge can be estimated by Canny Edge detection method. Original Image The edge detection can be applicable to Gray Image. Therfore the origianl RGB image can be converted to gray image. After

26、that Canny Edge Detection is applied for getting the Edges of the original image. Edges of original Image The original can be blurred using gaussian low pass filter by specifying the blur parameters. The following ima