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23 February 2019 Image denoising in impulsive noise via weighted Schatten p-norm regularization
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Abstract
Low-rank methods have been widely exploited in image denoising and have shown admirable denoising performance, of which weighted Schatten p-norm minimization (WSNM) is particularly effective. However, the WSNM method which applies Frobenius-norm loss model cannot obtain a satisfactory denoising performance when images corrupted by impulse noise. An optimization strategy based on the alternating direction method of multipliers framework is used to solve the proposed model efficiently. Experimental results show that the proposed method outperforms some state-of-the-art denoising methods both quantitatively and qualitatively under various impulsive noise models.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Gang Chen, Jianjun Wang, Feng Zhang, and Wendong Wang "Image denoising in impulsive noise via weighted Schatten p-norm regularization," Journal of Electronic Imaging 28(1), 013044 (23 February 2019). https://doi.org/10.1117/1.JEI.28.1.013044
Received: 24 September 2018; Accepted: 6 February 2019; Published: 23 February 2019
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