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Proceedings Paper

Deep learning for impulsive noise removal in color digital images
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Paper 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.

Paper Details

Date Published: 14 May 2019
PDF: 9 pages
Proc. SPIE 10996, Real-Time Image Processing and Deep Learning 2019, 1099608 (14 May 2019); doi: 10.1117/12.2519483
Show Author Affiliations
Krystian Radlak, Silesian Univ. of Technology (Poland)
Lukasz Malinski, Silesian Univ. of Technology (Poland)
Bogdan Smolka, Silesian Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10996:
Real-Time Image Processing and Deep Learning 2019
Nasser Kehtarnavaz; Matthias F. Carlsohn, Editor(s)

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