Journal
SENSORS
Volume 18, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/s18030789
Keywords
multi-scale; convolutional neural network; image super-resolution
Funding
- National Natural Science Foundation of China [61672335, 61601276, 61302174, 61571380]
- Natural Science Foundation of Fujian Province of China [2016J05205]
- Xiamen University of Technology [YKJ16018R, YKJ16020R]
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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.
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