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Open AccessArticle

Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations

1
School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2
School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China
3
Department of Electronic Science, Xiamen University, Xiamen 361005, China
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(1), 7; https://doi.org/10.3390/a12010007
Received: 1 December 2018 / Revised: 22 December 2018 / Accepted: 23 December 2018 / Published: 25 December 2018
(This article belongs to the Special Issue Dictionary Learning Algorithms and Applications)
Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L0-L0 norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria. View Full-Text
Keywords: impulse noise removal; sparse representation; dictionary learning; L0 norm impulse noise removal; sparse representation; dictionary learning; L0 norm
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Guo, D.; Tu, Z.; Wang, J.; Xiao, M.; Du, X.; Qu, X. Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations. Algorithms 2019, 12, 7.

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