- Research Article
- Open Access
Effective Image Restorations Using a Novel Spatial Adaptive Prior
EURASIP Journal on Advances in Signal Processing volume 2010, Article number: 508089 (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.
To access the full article, please see PDF.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
About this article
Cite this article
Chen, Y., Li, Y., Dong, Y. et al. Effective Image Restorations Using a Novel Spatial Adaptive Prior. EURASIP J. Adv. Signal Process. 2010, 508089 (2010). https://doi.org/10.1155/2010/508089
- Neighbor Pixel
- Image Information
- Image Restoration
- Prior Model