A convolutional neural networks denoising approach for salt and pepper noise

• Published in: Multimedia tools and applications Vol. 78; no. 21; pp. 30707 - 30721
• Main Authors: Fu, Bo, Zhao, Xiaoyang, Li, Yi, Wang, Xianghai, Ren, Yonggong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Multimedia Information Systems; Neural networks; Noise; Noise reduction; Special Purpose and Application-Based Systems; Switching theory; Training; Non-local switching filter; Convolutional neural networks; Image denoising; Salt and pepper noise
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-018-6521-4

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.