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http://dx.doi.org/10.9717/kmms.2017.20.11.1750

Fast Very Deep Convolutional Neural Network with Deconvolution for Super-Resolution  

Lee, Donghyeon (Inter-university Semiconductor Research Center (ISRC), Department of Electrical Engineering and Computer Science, Seoul National University)
Lee, Ho Seong (Inter-university Semiconductor Research Center (ISRC), Department of Electrical Engineering and Computer Science, Seoul National University)
Lee, Kyujoong (Dept. of Electronic Eng., School of Engineering, Sun Moon University)
Lee, Hyuk-Jae (Inter-university Semiconductor Research Center (ISRC), Department of Electrical Engineering and Computer Science, Seoul National University)
Publication Information
Journal of Korea Multimedia Society / v.20, no.11, 2017 , pp. 1750-1758 More about this Journal
Abstract
In super-resolution, various methods with Convolutional Neural Network(CNN) have recently been proposed. CNN based methods provide much higher image quality than conventional methods. Especially, VDSR outperforms other CNN based methods in terms of image quality. However, it requires a high computational complexity which prevents real-time processing. In this paper, the method to apply a deconvolution layer to VDSR is proposed to reduce computational complexity. Compared to original VDSR, the proposed method achieves the 4.46 times speed-up and its degradation in image quality is less than -0.1 dB which is negligible.
Keywords
Single Image Super Resolution; Convolutional Neural Network; Deep Learning; Complexity Reduction; Memory Reduction;
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Times Cited By KSCI : 1  (Citation Analysis)
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