IEEE Trans Image Process - Multiplicative noise removal via a learned dictionary.

Tópicos

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Resumo

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.

Resumo Limpo

multipl nois remov challeng imag process problem exist method base maximum posteriori formul logarithm transform multipl denois problem addit denois problem spars represent imag shown effici approach imag recoveri follow idea paper propos learn dictionari logarithm transform imag use variat model built nois remov extens experiment result suggest term visual qualiti peak signaltonois ratio mean absolut deviat error propos algorithm outperform stateoftheart method

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