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Pré-Publication, Document De Travail Année : 2006

Incorporate $TV-l^{\infty}$ model with Sparse Representations for Image Denoising, a post-processing approach

Résumé

Sparse representations of images have revoked remarkable interest recently. The assumption that natural images admit a sparse decomposition over a redundant dictionary leads to efficient algorithm for image processing. In particular, the K-SVD method has been recently proposed and shown to perform very well for gray-scale and color image denoising task (\cite{elada},\cite{melada}). Meanwhile, the $TV-l^{\infty}$ model with special choice of dictionary has been proved to be very effective for image restoration(\cite{zeng.2006},\cite{lm2004}). In this paper, we propose a hybrid model which combines these two methods and may be regarded as a post-processing procedure for K-SVD. Due to the excellent work of K-SVD and the fact that $TV-l^{\infty}$ can reconstruct lost information quickly, this hybrid model lead to a new state-of-art denoising performance.
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Dates et versions

hal-00120876 , version 1 (18-12-2006)

Identifiants

  • HAL Id : hal-00120876 , version 1

Citer

Tieyong Zeng. Incorporate $TV-l^{\infty}$ model with Sparse Representations for Image Denoising, a post-processing approach. 2006. ⟨hal-00120876⟩
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