Deep learning has been widely applied in many computer vision tasks due to its impressive capability of automatic feature extraction and classification. Recently, deep neural networks have been used in image denosing, but most of the proposed approaches were designed for Gaussian noise suppression. Therefore, in this paper, we address the problem of impulsive noise reduction in color images using Denoising Convolutional Neural Networks (DnCNN). This network architecture utilizes the concept of deep residual learning and is trained to learn the residual image instead of the directly denoised one. Our preliminary results show that direct application of DnCNN allows to achieve significantly better results than the state-of-the-art filters designed for impulsive noise in color images.

© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Krystian Radlak, Lukasz Malinski, and Bogdan Smolka, "Deep learning for impulsive noise removal in color digital images," Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 1099608 (Presented at SPIE Defense + Commercial Sensing: April 15, 2019; Published: 14 May 2019); https://doi.org/10.1117/12.2519483.