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Lightweight Network Based on Interleaved Group Convolution for Image Super-Resolution

Jiexin Zhang, HanWang , Jiaxin Luo, Zheng Zhang

Abstract


Deep learning has been successfully applied to single image super-resolution problems due to its high data fitting ability. However, the increasing depth and complexity of the network has brought about the disappearance of information, data volume and computational redundancy, and is not suitable for small devices. To solve these problems, we propose a new lightweight network model based on interleaved group convolution for single image super-resolution reconstruction. The core idea of this algorithm is to broaden the network structure by means of group convolution, enhance the sparsity of the convolution kernel, and achieve the purpose of reducing the amount of calculation and the amount of parameters. After a lot of experimental evaluation,we prove that our al- gorithm can achieve better results with a smaller number of parameters.

Keywords


Super-Resolution; Deep-learning; Lightwe- ight model; Residual Learning; Interleaved group convolution

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References


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DOI: http://dx.doi.org/10.18686/ahe.v7i13.8551

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