IEEE Trans Image Process - Parallel proximal algorithm for image restoration using hybrid regularization.

Tópicos

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Resumo

Regularization approaches have demonstrated their effectiveness for solving ill-posed problems. However, in the context of variational restoration methods, a challenging question remains, namely how to find a good regularizer. While total variation introduces staircase effects, wavelet-domain regularization brings other artefacts, e.g., ringing. However, a tradeoff can be made by introducing a hybrid regularization including several terms not necessarily acting in the same domain (e.g., spatial and wavelet transform domains). While this approach was shown to provide good results for solving deconvolution problems in the presence of additive Gaussian noise, an important issue is to efficiently deal with this hybrid regularization for more general noise models. To solve this problem, we adopt a convex optimization framework where the criterion to be minimized is split in the sum of more than two terms. For spatial domain regularization, isotropic or anisotropic total variation definitions using various gradient filters are considered. An accelerated version of the Parallel Proximal Algorithm is proposed to perform the minimization. Some difficulties in the computation of the proximity operators involved in this algorithm are also addressed in this paper. Numerical experiments performed in the context of Poisson data recovery, show the good behavior of the algorithm as well as promising results concerning the use of hybrid regularization techniques.

Resumo Limpo

regular approach demonstr effect solv illpos problem howev context variat restor method challeng question remain name find good regular total variat introduc staircas effect waveletdomain regular bring artefact eg ring howev tradeoff can made introduc hybrid regular includ sever term necessarili act domain eg spatial wavelet transform domain approach shown provid good result solv deconvolut problem presenc addit gaussian nois import issu effici deal hybrid regular general nois model solv problem adopt convex optim framework criterion minim split sum two term spatial domain regular isotrop anisotrop total variat definit use various gradient filter consid acceler version parallel proxim algorithm propos perform minim difficulti comput proxim oper involv algorithm also address paper numer experi perform context poisson data recoveri show good behavior algorithm well promis result concern use hybrid regular techniqu

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