A Dual Sparse Decomposition Method for Image Denoising

2017/04/24 by Hong Sun, Chenguang Liu, Chen-guang Liu +5
Computer Science · Engineering · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Sparse and Compressive Sensing Techniques #cs.CV

paper · pdf · doi:10.48550/arxiv.1704.07063

6 pages, 5 figures

arxiv created 2017/04/24 · openalex publication_date 2017/04/24 · arxiv updated 2017/04/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
• rm K-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

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