IEEE Trans Image Process - Nonlocal hierarchical dictionary learning using wavelets for image denoising.

Tópicos

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Resumo

Exploiting the sparsity within representation models for images is critical for image denoising. The best currently available denoising methods take advantage of the sparsity from image self-similarity, pre-learned, and fixed representations. Most of these methods, however, still have difficulties in tackling high noise levels or noise models other than Gaussian. In this paper, the multiresolution structure and sparsity of wavelets are employed by nonlocal dictionary learning in each decomposition level of the wavelets. Experimental results show that our proposed method outperforms two state-of-the-art image denoising algorithms on higher noise levels. Furthermore, our approach is more adaptive to the less extensively researched uniform noise.

Resumo Limpo

exploit sparsiti within represent model imag critic imag denois best current avail denois method take advantag sparsiti imag selfsimilar prelearn fix represent method howev still difficulti tackl high nois level nois model gaussian paper multiresolut structur sparsiti wavelet employ nonloc dictionari learn decomposit level wavelet experiment result show propos method outperform two stateoftheart imag denois algorithm higher nois level furthermor approach adapt less extens research uniform nois

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