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Communication Dans Un Congrès Année : 2017

Patch-based Image Denoising: Probability Distribution Estimation vs. Sparsity Prior

Résumé

Patch-based image denoising can be interpreted under the Bayesian framework which incorporates the image formation model and a prior image distribution. In the sparsity approach, the prior is often assumed to obey an arbitrarily chosen distribution. Our motivation is to estimate the probability directly from the distribution of image patches extracted from good quality images, thanks to a given dictionary and the redundancy of information between many images. In this paper, we provide a scheme to estimate the probability distribution and also an optimized algorithm for denoising. We demonstrate that using the estimated probability distribution as the image prior is more efficient than the state-of-the-art sparsity models for noise removal.
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Dates et versions

hal-03949203 , version 1 (20-01-2023)

Identifiants

Citer

Dai-Viet Tran, Sébastien Li-Thiao-Té, Marie Luong, Thuong Le-Tien, Francoise Dibos. Patch-based Image Denoising: Probability Distribution Estimation vs. Sparsity Prior. 25th European Signal Processing Conference (EUSIPCO 2017), Aug 2017, Kos, Greece. pp.1490-1494, ⟨10.23919/EUSIPCO.2017.8081457⟩. ⟨hal-03949203⟩
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