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Edge preserving mixed noise removal

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Abstract

To faithfully recover the clean images corrupted by additive white Gaussian noise (AWGN) and impulse noise (IN), a novel edge preserving image denoising algorithm is proposed. The low- and high-frequency components of the image are restored separately. The high-frequency components of the images are restored based on nonlocal self-similarity (NSS) learning from natural images. An energy minimization function is developed to combine the low- and high-frequency components into one model. Experiments demonstrate that the proposed method outperforms existing mixture noise removal methods in peak signal-to-noise ratio (PSNR), edges preservation and visual performance.

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Acknowledgements

The authors would like to thank the reviewers for their invaluable comments. This work was supported partly by National Natural Science Foundation of China (No. 61772312, 61602277), NSFC Joint Fund with Zhejiang Integration of Informatization and Industrialization under Key Project(U1609218) and Natural Science Foundation of Shandong Province, China (No. ZR2017MF033).

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  1. School of Software, Shandong University, Jinan, 250101, People’s Republic of China

    Fenghua Guo & Caiming Zhang

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  1. Fenghua Guo
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  2. Caiming Zhang
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Correspondence to Caiming Zhang.

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Guo, F., Zhang, C. Edge preserving mixed noise removal. Multimed Tools Appl 78, 16601–16613 (2019). https://doi.org/10.1007/s11042-018-7004-3

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