Abstract
During the process of acquisition and transmission, images are often likely to be corrupted by mixed Gaussian-impulse noise. Among various image denoising methods, most traditional methods can only deal with a single type of noise due to the difficulty of modeling the distribution of the mixed noise. In this paper, we propose a novel mixed Gaussian-impulse noise removal method based on adaptive integration skip compensation Network (Ai-Sc-Net). More concretely, a couple of skip compensation networks (Sc-Net) Sc-Net-AWGN and Sc-Net-IN are trained on Gaussian and Impulse noise datasets separately to deal with the corresponding single type noise. Further, an adaptive integration network (Ai-Net) is used to integrate the two outputs of Sc-Net-AWGN and Sc-Net-IN. The Ai-Sc-Net is then be constructed based on Sc-Net and Ai-Net, which can handle mixed noise. Experimental results in synthetic noise images have shown great improvements over several state-of-the-art mixed noise removal methods.
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Acknowledgment
This work was supported in part by the Project of National Engineering Laboratory for Video Technology - Shenzhen Division, in part by Shenzhen Key Laboratory for Intelligent Multimedia and Virtual Reality under Grant ZDSYS 201703031405467, and in part by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing) under Grant 1230233753.
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Lin, K., Zhang, Y., Li, T.H., Huang, K., Li, G. (2018). Adaptive Integration Skip Compensation Neural Networks for Removing Mixed Noise in Image. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_35
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