Deep learning for impulsive noise removal in color digital images

Krystian Radlak; Lukasz Malinski; Bogdan Smolka

doi:10.1117/12.2519483

14 May 2019 Deep learning for impulsive noise removal in color digital images

Krystian Radlak, Lukasz Malinski, Bogdan Smolka

Proceedings Volume 10996, Real-Time Image Processing and Deep Learning 2019; 1099608 (2019) https://doi.org/10.1117/12.2519483
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

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.

Conference Presentation

Citation Download Citation

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 (14 May 2019); https://doi.org/10.1117/12.2519483

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