Abstract
In this paper, we demonstrate that not only natural images but also their intermediate responses of convolutional neural networks (CNNs) have local smoothing priors. To imposing the local smoothing constraint, we design a local smoothing layer, which is able to suppress noises in a local receptive field. Further, we arrange the local smoothing layer in the early layers of CNNs to effectively capture context information, which is helpful to recovery image details. Experimental results validate that the proposed denoising method outperforms several state-of-the-art methods.
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Guo, Y., Jiang, F., Zhao, D., Wen, Z., Liu, S. (2019). Local Smoothing Constraint in Convolutional Neural Network for Image Denoising. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_36
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DOI: https://doi.org/10.1007/978-3-030-24274-9_36
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