1. Blocking sparse method for image denoising
- Author
-
Jianjun Yuan and Jiao He
- Subjects
business.industry ,Computer science ,Noise reduction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,020207 software engineering ,Data_CODINGANDINFORMATIONTHEORY ,02 engineering and technology ,Blocking (statistics) ,Image (mathematics) ,ComputingMethodologies_PATTERNRECOGNITION ,Compressed sensing ,Artificial Intelligence ,Computer Science::Computer Vision and Pattern Recognition ,Pattern recognition (psychology) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computer vision ,Computer Vision and Pattern Recognition ,Artificial intelligence ,Image denoising ,business ,Block (data storage) - Abstract
In recent years, compressive sensing has been one promising technique for denoising images. This paper presents a new denoising model based on blocking sparsity. First, an image is blocked. Second, the split-Bregman method is used to solve for each block image. Finally, all denoised block images are combined into one image. Compared with the latest HTV, GHNS, FastATV, CSR and BM3D models, experimental results demonstrate that the proposed method is efficient, and has better denoising capability.
- Published
- 2021
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