IEEE Trans Image Process - Mixed noise removal by weighted encoding with sparse nonlocal regularization.

Tópicos

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Resumo

Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.

Resumo Limpo

mix nois remov natur imag challeng task sinc nois distribut usual parametr model heavi tail one typic kind mix nois addit white gaussian nois awgn coupl impuls nois mani mix nois remov method detect base method first detect locat pixel remov mix nois howev method tend generat mani artifact mix nois strong paper propos simpl yet effect method name weight encod spars nonloc regular wesnr mix nois remov wesnr explicit step impuls pixel detect instead soft impuls pixel detect via weight encod use deal awgn simultan meanwhil imag sparsiti prior nonloc selfsimilar prior integr regular term introduc variat encod framework experiment result show propos wesnr method achiev lead mix nois remov perform term quantit measur visual qualiti

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