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arXiv · 1807.08176

A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise

Bo Fu·Xiao-Yang Zhao·Yi Li·Xiang-Hai Wang·Yong-Gong Ren
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Abstract

The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.

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Bo Fu, Xiao-Yang Zhao, Yi Li, Xiang-Hai Wang, Yong-Gong Ren. 2018-07-21. A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise. https://arxiv.org/abs/1807.08176

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