Woojin Jeong, Bok Gyu Han, Dong Seok Lee, Byung In Choi, Young Shik Moon, Journal of Internet Computing and Services, Vol. 19, No. 4, pp. 45-52, Aug. 2018
10.7472/jksii.2018.19.4.45, Full Text:  HTML
Keywords: Image super-resolution, deep neural network, Convolutional Neural Network

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.

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