IEEE Trans Image Process - Context-aware sparse decomposition for image denoising and super-resolution.

Tópicos

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Resumo

Image prior models based on sparse and redundant representations are attracting more and more attention in the field of image restoration. The conventional sparsity-based methods enforce sparsity prior on small image patches independently. Unfortunately, these works neglected the contextual information between sparse representations of neighboring image patches. It limits the modeling capability of sparsity-based image prior, especially when the major structural information of the source image is lost in the following serious degradation process. In this paper, we utilize the contextual information of local patches (denoted as context-aware sparsity prior) to enhance the performance of sparsity-based restoration method. In addition, a unified framework based on the markov random fields model is proposed to tune the local prior into a global one to deal with arbitrary size images. An iterative numerical solution is presented to solve the joint problem of model parameters estimation and sparse recovery. Finally, the experimental results on image denoising and super-resolution demonstrate the effectiveness and robustness of the proposed context-aware method.

Resumo Limpo

imag prior model base spars redund represent attract attent field imag restor convent sparsitybas method enforc sparsiti prior small imag patch independ unfortun work neglect contextu inform spars represent neighbor imag patch limit model capabl sparsitybas imag prior especi major structur inform sourc imag lost follow serious degrad process paper util contextu inform local patch denot contextawar sparsiti prior enhanc perform sparsitybas restor method addit unifi framework base markov random field model propos tune local prior global one deal arbitrari size imag iter numer solut present solv joint problem model paramet estim spars recoveri final experiment result imag denois superresolut demonstr effect robust propos contextawar method

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