Open Access

Effective Image Restorations Using a Novel Spatial Adaptive Prior

  • Yang Chen1, 2,
  • Yinsheng Li1, 2,
  • Yingmei Dong3,
  • Liwei Hao2,
  • Limin Luo1 and
  • Wufan Chen2Email author
EURASIP Journal on Advances in Signal Processing20102010:508089

Received: 20 October 2009

Accepted: 16 February 2010

Published: 24 May 2010


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.


DeconvolutionNeighbor PixelImage InformationImage RestorationPrior Model

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

The Laboratory of Image Science and Technology, Southeast University, Nanjing, China
The School of Biomedical Engineering, Southern Medical University, Guangzhou, China
Cadre Reset Institute, The Joint Logistics Department, Chengdu Military Region, Chengdu, China


© Yang Chen et al. 2010

This article is published under license to BioMed Central Ltd. 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.