Mixed Noise Removal by Bilateral Weighted Sparse Representation

Mixed Noise Removal by Bilateral Weighted Sparse Representation

Image denoising is a fundamental but difficult task in image processing. Recovering a clean version from an image corrupted by mixed Gaussian and impulse noise is still challenging. The weighted sparse representation (WSR) method has been applied to deal with mixed noise and has achieved good perfor...

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Bibliographic Details
Published in:Circuits, systems, and signal processing Vol. 40; no. 9; pp. 4490 - 4515
Main Authors: Sheng, Jiechao, Lv, Guoqiang, Xue, Zhitian, Wu, Lei, Feng, Qibin
Format: Journal Article
Language:English
Published: New York Springer US 01-09-2021
Springer Nature B.V
Subjects:
Algorithms
Circuits and Systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Image processing
Instrumentation
Noise
Performance evaluation
Regularization
Representations
Signal,Image and Speech Processing
Visual effects
Sparse representation
Method noise regularization
Gaussian noise
Mixed noise
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Summary:Image denoising is a fundamental but difficult task in image processing. Recovering a clean version from an image corrupted by mixed Gaussian and impulse noise is still challenging. The weighted sparse representation (WSR) method has been applied to deal with mixed noise and has achieved good performance. However, the WSR model presents an obvious disadvantage due to oversmoothing of the texture regions. In this paper, the novel bilateral weighted sparse representation model is presented for mixed noise removal. By introducing an image gradient-based weight, an adaptive sparse ratio is provided for each image patch to protect additional image details. In addition, a weighted method noise regularization term is proposed to utilize the global image information submerged in the method noise, which can improve the visual effect of recovered noise-free images. Both the subjective and objective performance are evaluated, and the experimental results show that the proposed method outperforms existing mixed noise removal algorithms, especially in terms of the visual performance.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-021-01677-x