The Study and Simulation of the Fabric Defects Images Restoration

Article Preview

Abstract:

By using non-negativity and support constraints recursive inverse filtering ,blind image restoration can be realized. But it’s difficult to resolve the proplem of sensitive to noise,so there has been no practical application. In this paper,according to the characteristic of NAS-RIF algorithm and the question of fuzzy by fabric defects images moving at a high-speed line, methods have been introduced, firstly, Adopt boundary keep smoothing filter as the de-noising pretreatment was carried out for image signal; Secondly, in each iteration of restoration,add the low-pass filter link。Thus,the problem of NAS-RIF algorithm is solved well.The simulation results show that this method has a satisfactory outcome both in visual impression and quantitative analysis.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

88-91

Citation:

Online since:

November 2011

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Nuer dun; Zuo Baoqi. Textile Inspection and Image Recognition. Journal of Soochow University (Engineering Science Edition), 2003. 4, 23(2)49~54.

Google Scholar

[2] A. Bodnarova, M. Bennamoun, and K. Kubik. Suitability ananlysis of techniques for flaw detection in textiles using texture analysis. Pattern Analysis and Application, to appear in Auguest (2000).

DOI: 10.1007/s100440070010

Google Scholar

[3] Robert Drobina, Mieczyslaw S. Machnio. Application of the image analysis technique for textile identification[J]. Autex research Journal, 2006, 6(1): 40-48.

Google Scholar

[4] Zhang Hang, Luo Dayong. Status and Development of Study on Blind Image Restoration Algorithm. [J]Journal of Image and Graphics, 2004, 9: 1145-1149.

Google Scholar

[5] Xue Renan; Zhang Zhifeng. The Algorithm and Implementation of Image Blind Restoration. [J] Journal of Hangzhou Dianzi University, 2005, 25(4): 90-94.

Google Scholar

[6] Zhang Defeng etc. MATLAB Digital image processing[M]. Beijing: China machine press, (2009).

Google Scholar

[7] Ayers GR, Dainty J C. Iterative Blind Deconvolution Method and Its Applications[J]. Optics Letter, 1988, 13(7): 547-549.

DOI: 10.1364/ol.13.000547

Google Scholar

[8] McCallum C. Blind Deconvolution by Simulate Annealing[J]. Optics Communications, 1990, 75(2): 101-105.

DOI: 10.1016/0030-4018(90)90236-m

Google Scholar

[9] Noriaki Miura. Blind deconvolution under band limitation [J]. Optics Letters. 2003, 28(23): 2312-2314.

DOI: 10.1364/ol.28.002312

Google Scholar

[10] Ayers G, Dainty. Iterative blind deconvolution method and its applications [J]. Optics, 1998, 13(7): 547-549.

DOI: 10.1364/ol.13.000547

Google Scholar

[11] Kundur D, Hazinakos D. A novel blind deconvolution scheme for image restoration using recursive filtering[J]. IEEE Transactions on Signal Processing, 1998, 46(2): 156-161.

DOI: 10.1109/78.655423

Google Scholar

[12] Mu Xiaofang, Zhao Yueai, Zhang Zhaoxiao, Deng Hongxia. An Improved NAS-RIF Algorithm for Blind Image Restoration, Journal of Taiyuan Normal.

Google Scholar