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http://dx.doi.org/10.7472/jksii.2018.19.4.45

Study of Efficient Network Structure for Real-time Image Super-Resolution  

Jeong, Woojin (Department of computer science and engineering, Hanyang University)
Han, Bok Gyu (Department of computer science and engineering, Hanyang University)
Lee, Dong Seok (Image PGM Team, Hanwha Systems)
Choi, Byung In (Image PGM Team, Hanwha Systems)
Moon, Young Shik (Department of computer science and engineering, Hanyang University)
Publication Information
Journal of Internet Computing and Services / v.19, no.4, 2018 , pp. 45-52 More about this Journal
Abstract
A single-image super-resolution is a process of restoring a high-resolution image from a low-resolution image. Recently, the super-resolution using the deep neural network has shown good results. In this paper, we propose a neural network structure that improves speed and performance over conventional neural network based super-resolution methods. To do this, we analyze the conventional neural network based super-resolution methods and propose solutions. The proposed method reduce the 5 stages of the conventional method to 3 stages. Then we have studied the optimal width and depth by experimenting on the width and depth of the network. Experimental results have shown that the proposed method improves the disadvantages of the conventional methods. The proposed neural network structure showed superior performance and speed than the conventional method.
Keywords
Image super-resolution; deep neural network; convolutional neural network;
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