Abstract
Deep convolutional neural networks (CNNs) have been extensively applied on single image super-resolution (SISR) due to the strong representation. However, since SISR is an ill-posed problem, many CNN-based methods rely heavily on excessive parameters and high computation cost, which limit the practical application on devices with limited resources. In this paper, a light-weight yet effective multi-channel aggregation network (MCAN) is proposed to improve the performance of SISR while maintaining efficiency. Specifically, the network is built upon several efficient multi-channel aggregation blocks, each of which contains a multi-channel aggregation (MCA) module and a dilated attention (DA) module. The proposed MCA module reduces network parameters considerably, moreover, the channel split and concatenation operations ensure multi-channel interaction and enrich multi-scale features effectively. Furthermore, the DA module captures multiple spatial feature correlations using multi-scale dilated convolution for a larger receptive field. Experimental results on four publicly available datasets demonstrate the superiority of the proposed MCAN in terms of accuracy and model complexity.
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Bian, P., Zheng, Z., Zhang, D. (2021). Light-Weight Multi-channel Aggregation Network for Image Super-Resolution. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13021. Springer, Cham. https://doi.org/10.1007/978-3-030-88010-1_24
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