Super-resolution for noisy images via deep convolutional neural network

Xinyan Zhang; Peng Gao; Sunxiangyu Liu; Kongya Zhao; Guitao Li; Liuguo Yin

doi:10.1117/12.2297664

24 May 2018 Super-resolution for noisy images via deep convolutional neural network

Xinyan Zhang, Peng Gao, Sunxiangyu Liu, Kongya Zhao, Guitao Li, Liuguo Yin

Proceedings Volume 10677, Unconventional Optical Imaging; 1067710 (2018) https://doi.org/10.1117/12.2297664
Event: SPIE Photonics Europe, 2018, Strasbourg, France

Abstract

Super-resolution (SR) is an effective approach to enhance image spatial resolution. Although many SR algorithms have been proposed by far, little progress has been made to improve resolution for a noisy image. Conventional approaches always adopt the denoising step before applying the SR method to noisy low-resolution images. However, some high-frequency details lose during the denoising step and cannot be restored by the following SR step. Therefore, motivated by the success of deep learning in different computer vision missions, we propose a novel method named Denoising Super-Resolution Deep Convolutional Network (DSR-DCN), to combine both denoising and SR step in a single deep model. The proposed deep model straightly learns an end-to-end mapping from noisy LR space to the corresponding HR space. To equip the proposed network with the capability of blind denoising, Gaussian noise, with a range of standard deviation instead of constant value, is added to each patch of the LR space during training. Experiment results demonstrate that DSR-DCN achieves superior performance and better visual effects than the conventional approaches.

Citation Download Citation

Xinyan Zhang, Peng Gao, Sunxiangyu Liu, Kongya Zhao, Guitao Li, and Liuguo Yin "Super-resolution for noisy images via deep convolutional neural network", Proc. SPIE 10677, Unconventional Optical Imaging, 1067710 (24 May 2018); https://doi.org/10.1117/12.2297664

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KEYWORDS

Super resolution

Denoising

Convolution

Feature extraction

Nonlinear filtering

Convolutional neural networks

Deconvolution

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