A Dual Sparse Decomposition Method for Image Denoising
This addresses image denoising for applications like photography or medical imaging, but it appears incremental as it builds on existing sparse decomposition techniques.
The paper tackles image denoising under strong noise by proposing a dual sparse decomposition method, which improves denoising performance as demonstrated by surpassing state-of-the-art results in PSNR and SSIM metrics.
This article addresses the image denoising problem in the situations of strong noise. We propose a dual sparse decomposition method. This method makes a sub-dictionary decomposition on the over-complete dictionary in the sparse decomposition. The sub-dictionary decomposition makes use of a novel criterion based on the occurrence frequency of atoms of the over-complete dictionary over the data set. The experimental results demonstrate that the dual-sparse-decomposition method surpasses state-of-art denoising performance in terms of both peak-signal-to-noise ratio and structural-similarity-index-metric, and also at subjective visual quality.