Mixed impulse and Gaussian noise removal using L0 sparse regularization

Zhenxing Liu; Xueying Zeng

doi:10.1117/12.2589376

27 January 2021 Mixed impulse and Gaussian noise removal using L0 sparse regularization

Zhenxing Liu, Xueying Zeng

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 117202B (2021) https://doi.org/10.1117/12.2589376
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

This paper considers the problem of denoising the image corrupted by mixed impulse and Gaussian noise. We propose a two-stage approach based on impulse detectors and L0 sparse regularization. We first employ the impulse detectors to identify the locations of impulse noise, and then restore the noisy image by solving a constrained minimization model. The objective function of the proposed model uses the L0 norm to promote the sparsity of the resulting image in a tight framelet system. To overcome the algorithmic difficulty caused by the L0 norm, we use proximal block coordinate descent method to solve an approximate model. The global convergence of the algorithm is proved. We also develop an adaptive strategy on approximation parameter selection and a FISTA-like iterative scheme to speed up the algorithm. Numerical results show that our method performs favorably in comparison to several existing algorithms.

Citation Download Citation

Zhenxing Liu and Xueying Zeng "Mixed impulse and Gaussian noise removal using L0 sparse regularization", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 117202B (27 January 2021); https://doi.org/10.1117/12.2589376

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