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Mathematical Problems in Engineering
Volume 2014, Article ID 905189, 10 pages
Research Article

Blind Image Restoration via the Integration of Stochastic and Deterministic Methods

Department of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Received 6 March 2014; Revised 21 April 2014; Accepted 21 April 2014; Published 15 May 2014

Academic Editor: Ming Li

Copyright © 2014 Yi-bing Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper addresses the image restoration problem which remains a significant field of image processing. The fields of experts- (FoE-) based image restoration has been discussed and some open issues including noise estimation and parameter selection have been approached. The stochastic method FoE performs fairly well; meanwhile it might also produce unsatisfactory outcome especially when the noise is grave. To improve the final performance, we introduce the integration with deterministic method K-SVD. The FoE-treated image has been used to obtain the dictionary, and with the help of sparse and redundant representation over trained dictionary, the K-SVD algorithm can dramatically solve the problem, even though the pretreated result is of poor quality under severe noise condition. The experimental results via our proposed method are demonstrated and compared in detail. Meanwhile the test results from both qualitative and quantitative aspects are given, which present the better performance over current state-of-art related restoration algorithms.