Effective Image Restorations Using a Novel Spatial Adaptive Prior. (24th May 2010)

• Record Type: Journal Article
• Authors: Chen Chen, Yang Yang; Li Li, Yinsheng Yinsheng; Dong Dong, Yingmei Yingmei; Hao Hao, Liwei Liwei; Luo Luo, Limin Limin; Chen Chen, Wufan Wufan
• Other Names: Chen Chen Liang-Gee Liang-Gee Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-05-24
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/508089
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : Bayesian or Maximum a posteriori (MAP) approaches can effectively overcome the ill-posed problems of image restoration or deconvolution through incorporating a priori image information. Many restoration methods, such as nonquadratic prior Bayesian restoration and total variation regularization, have been proposed with edge-preserving and noise-removing properties. However, these methods are often inefficient in restoring continuous variation region and suppressing block artifacts. To handle this, this paper proposes a Bayesian restoration approach with a novel spatial adaptive (SA) prior. Through selectively and adaptively incorporating the nonlocal image information into the SA prior model, the proposed method effectively suppress the negative disturbance from irrelevant neighbor pixels, and utilizes the positive regularization from the relevant ones. A two-step restoration algorithm for the proposed approach is also given. Comparative experimentation and analysis demonstrate that, bearing high-quality edge-preserving and noise-removing properties, the proposed restoration also has good deblocking property.