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2014 IEEE International Conference on Image Processing (ICIP)

DOI: 10.1109/icip.2014.7025908

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Non-Blind Image Restoration With Symmetric Generalized Pareto Priors

Proceedings article published in 2014 by Xing Mei, Bao-Gang Hu, Siwei Lyu ORCID
This paper is available in a repository.
This paper is available in a repository.

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Abstract

This paper presents a new non-blind image restoration method based on the symmetric generalized Pareto (SGP) prior, which models the heavy-tailed distributions of gradients for natural images. Through experiments we show that the SGP model achieves log likelihood scores comparable to the hyper-Laplacian model when fitted to gradients and other band-pass filter responses. More importantly, when incorpo-rated into a Bayesian MAP framework for non-blind image restoration, the SGP model leads to a closed-form solution for a per-pixel subproblem, which affords computational advantages in comparison with the numerical solutions in-duced from the hyper-Laplacian model. Experimental results show that our method is comparable to existing methods in restoration quality and processing speed.