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Algorithms 2018, 11(10), 144; https://doi.org/10.3390/a11100144

Multi-Branch Deep Residual Network for Single Image Super-Resolution

1,2,3,* , 1,2
and
1,2
1
Key Laboratory of Information Technology for Autonomous Underwater Vehicles, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Received: 21 August 2018 / Revised: 23 September 2018 / Accepted: 25 September 2018 / Published: 27 September 2018
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Abstract

Recently, algorithms based on the deep neural networks and residual networks have been applied for super-resolution and exhibited excellent performance. In this paper, a multi-branch deep residual network for single image super-resolution (MRSR) is proposed. In the network, we adopt a multi-branch network framework and further optimize the structure of residual network. By using residual blocks and filters reasonably, the model size is greatly expanded while the stable training is also guaranteed. Besides, a perceptual evaluation function, which contains three parts of loss, is proposed. The experiment results show that the evaluation function provides great support for the quality of reconstruction and the competitive performance. The proposed method mainly uses three steps of feature extraction, mapping, and reconstruction to complete the super-resolution reconstruction and shows superior performance than other state-of-the-art super-resolution methods on benchmark datasets. View Full-Text
Keywords: single image super-resolution; deep neural networks; residual networks; peak signal-to-noise ratio; structural similarity index single image super-resolution; deep neural networks; residual networks; peak signal-to-noise ratio; structural similarity index
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Liu, P.; Hong, Y.; Liu, Y. Multi-Branch Deep Residual Network for Single Image Super-Resolution. Algorithms 2018, 11, 144.

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