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A Lightweight Local-Global Attention Network for Single Image Super-Resolution
For a given image, the self-attention mechanism aims to capture dependencies for each pixel. It has been proved that the performance of neural networks which employ self-attention is superior in various image processing tasks. However, the performance of self-attention has extensively correlated with the amount of computation. The vast majority of works tend to use local attention to capture local information to reduce the amount of calculation when using self-attention. The ability to capture information from the entire image is easily weakened on this occasion. In this paper, a local-global attention block (LGAB) is proposed to enhance both the local features and global features with low calculation complexity. To verify the performance of LGAB, a lightweight local-global attention network (LGAN) for single image super-resolution (SISR) is proposed and evaluated. Compared with other lightweight state-of-the-arts (SOTAs) of SISR, the superiority of our LGAN is demonstrated by extensive experimental results. The source code can be found at https://github.com/songzijiang/LGAN.