IEEE Trans Image Process - Nonlocal image restoration with bilateral variance estimation: a low-rank approach.

Tópicos

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Resumo

Simultaneous sparse coding (SSC) or nonlocal image representation has shown great potential in various low-level vision tasks, leading to several state-of-the-art image restoration techniques, including BM3D and LSSC. However, it still lacks a physically plausible explanation about why SSC is a better model than conventional sparse coding for the class of natural images. Meanwhile, the problem of sparsity optimization, especially when tangled with dictionary learning, is computationally difficult to solve. In this paper, we take a low-rank approach toward SSC and provide a conceptually simple interpretation from a bilateral variance estimation perspective, namely that singular-value decomposition of similar packed patches can be viewed as pooling both local and nonlocal information for estimating signal variances. Such perspective inspires us to develop a new class of image restoration algorithms called spatially adaptive iterative singular-value thresholding (SAIST). For noise data, SAIST generalizes the celebrated BayesShrink from local to nonlocal models; for incomplete data, SAIST extends previous deterministic annealing-based solution to sparsity optimization through incorporating the idea of dictionary learning. In addition to conceptual simplicity and computational efficiency, SAIST has achieved highly competent (often better) objective performance compared to several state-of-the-art methods in image denoising and completion experiments. Our subjective quality results compare favorably with those obtained by existing techniques, especially at high noise levels and with a large amount of missing data.

Resumo Limpo

simultan spars code ssc nonloc imag represent shown great potenti various lowlevel vision task lead sever stateoftheart imag restor techniqu includ bmd lssc howev still lack physic plausibl explan ssc better model convent spars code class natur imag meanwhil problem sparsiti optim especi tangl dictionari learn comput difficult solv paper take lowrank approach toward ssc provid conceptu simpl interpret bilater varianc estim perspect name singularvalu decomposit similar pack patch can view pool local nonloc inform estim signal varianc perspect inspir us develop new class imag restor algorithm call spatial adapt iter singularvalu threshold saist nois data saist general celebr bayesshrink local nonloc model incomplet data saist extend previous determinist annealingbas solut sparsiti optim incorpor idea dictionari learn addit conceptu simplic comput effici saist achiev high compet often better object perform compar sever stateoftheart method imag denois complet experi subject qualiti result compar favor obtain exist techniqu especi high nois level larg amount miss data

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