外文翻译---模糊图像的盲反卷积复原算法

《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《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、 PDF外文：http:/  新疆农业大学  英文文献翻译       题    目 : Restoration of Blurred Images Using Blind  Deconvolution Algortithm                                    姓    名 : 张凡

2、                                      学    院 : 计算机与信息工程学院                                       专 &n

3、bsp;  业 : 信息管理与信 息系统                                       班    级 : 082                                  

4、    学    号 : 084631201                                     指 导教师 : 罗江岩                  职称 : 讲师              

5、   2012 年 5 月 15 日  新疆农业大学教务处制   1 Restoration of Blurred Images Using Blind Deconvolution Algorithm Ms.S.Ramya Kalasalingam University, Anand Nagar, Krishnankoil  Ms.T.Mercy Christial Kalasalingam University, Anand Nagar, Krishnankoil Abstract: Image restoration is the proc

6、ess 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

7、, 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 proc

8、ess. 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 o

9、ur proposed Blind Deconvolution Algorithm. Key words：  Blind Deconvolution Algorithm； Canny Edge Detection； 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.

10、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 imperf

11、ect 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  2 low-pass filter is used to blur/smooth the image using certain functions. Image restoration is to improve the quali

12、ty 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. T

13、here 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 Fun

14、ction). 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 deg

15、radation 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, comparis

16、on and future work. 2 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 PSF which is a blurring function. The degraded image can be described by the following e

17、quation (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.