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Sensors 2018, 18(3), 789; doi:10.3390/s18030789

Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network

School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Department of Electronic Science, Xiamen University, Xiamen 361005, China
Author to whom correspondence should be addressed.
Received: 12 December 2017 / Revised: 23 February 2018 / Accepted: 26 February 2018 / Published: 6 March 2018
(This article belongs to the Section Intelligent Sensors)
View Full-Text   |   Download PDF [2304 KB, uploaded 7 March 2018]   |  


Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction. Our experimental results on image super-resolution show that the performance of the proposed network outperforms the state-of-the-art methods. View Full-Text
Keywords: multi-scale; convolutional neural network; image super-resolution multi-scale; convolutional neural network; image super-resolution

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Du, X.; Qu, X.; He, Y.; Guo, D. Single Image Super-Resolution Based on Multi-Scale Competitive Convolutional Neural Network. Sensors 2018, 18, 789.

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