图像处理外文翻译---模糊图像的盲反卷积复原算法

《图像处理外文翻译---模糊图像的盲反卷积复原算法》由会员分享，可在线阅读，更多相关《图像处理外文翻译---模糊图像的盲反卷积复原算法（20页珍藏版）》请在毕设资料网上搜索。

1、新疆农业大学  英文文献翻译   题    目 : Restoration of Blurred Images Using Blind Deconvolution Algortithm                                    姓    名 :             &

2、nbsp;                         学    院 :                                       专    业 :           &nbs

3、p;                          班    级 :                                       学    号 :           &

4、nbsp;                          指 导教师 :                  职称 :                 新疆农业大学教务处制   1 Restoration of Blurred Images Using Blind Deconvoluti

5、on Algorithm Ms.S.Ramya Kalasalingam University, Anand Nagar, Krishnankoil  Ms.T.Mercy Christial Kalasalingam University, Anand Nagar, Krishnankoil Abstract: Image restoration is the process of recovering the original image from the degraded image. Aspire of the project is to restore the blurre

6、d/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 l

7、ow-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

8、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 Detect

9、ion； Degradation Model； Image restoration； PML； PSF 1 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 spaceinvariant or space-in variant. Image deblurring methods can be divided

10、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,

11、 an image can be degraded using low-pass filters and its noise. This  2 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 ob

12、jective 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 Decon

13、volution . 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 wi

14、th 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 deb

15、lurring 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 Model In degradation model, the image is blurred using filters and additiv

16、e 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) *g H f n                                          （ 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.