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 twostep 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.
Highlights
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
Throughout this paper we assume that the degradation model Point-spread function (PSF) A and noise variance σ2 are known for they could be numerically estimated or calibrated
The TWIST [7] and Newton-Raphson algorithm are used in restorations using total variation (TV) prior and Huber prior, respectively
Summary
The simple and widely used quadratic membrane (QM) prior or the Tikhonov L2 regularization, which smoothes both noise and edge details leads to a linear inversion process and tends to produce an unfavorable oversmoothing effect [5] To solve this oversmoothing problem, many edgepreserving Bayesian restoration methods were proposed in the past twenty years. The weighting matrices in nonquadratic prior Bayesian restoration preserve edges by turning off or suppressing smoothing at appropriate locations Another impressive recent advance in this area is the total variation- (TV-) based image restoration algorithms [7, 14, 15]. This new restoration approach utilizes a spatial adaptive (SA) prior, which exploits the global connectivity and continuity information in the objective image It works by adaptively including the useful relevant neighbor pixels and excluding the negative irrelevant ones within a large prior neighborhood.
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