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http://dx.doi.org/10.5909/JBE.2019.24.4.633

Single Image Super-resolution using Recursive Residual Architecture Via Dense Skip Connections  

Chen, Jian (Department of Electronic Computer Engineering, Hanyang University)
Jeong, Jechang (Department of Electronic Computer Engineering, Hanyang University)
Publication Information
Journal of Broadcast Engineering / v.24, no.4, 2019 , pp. 633-642 More about this Journal
Abstract
Recently, the convolution neural network (CNN) model at a single image super-resolution (SISR) have been very successful. The residual learning method can improve training stability and network performance in CNN. In this paper, we propose a SISR using recursive residual network architecture by introducing dense skip connections for learning nonlinear mapping from low-resolution input image to high-resolution target image. The proposed SISR method adopts a method of the recursive residual learning to mitigate the difficulty of the deep network training and remove unnecessary modules for easier to optimize in CNN layers because of the concise and compact recursive network via dense skip connection method. The proposed method not only alleviates the vanishing-gradient problem of a very deep network, but also get the outstanding performance with low complexity of neural network, which allows the neural network to perform training, thereby exhibiting improved performance of SISR method.
Keywords
single image super-resolution; convolution neural network; residual learning; dense skip connection;
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