Remote Sensing Image Super-Resolution With Residual Split Attention Mechanism

Recently, deep-learning-based methods have become the current mainstream of remote sensing image super-resolution (SR) due to their powerful fitting ability. However, they are still unsatisfactory in large-scale factor SR scenarios. The more complicated information distribution of images further increases the difficulty of reconstruction. In this paper, we propose a novel residual split attention group (RSAG) to maintain the overall structural and the local details simultaneously. Specifically, an upscale module that makes the network jointly consider hierarchical priors, which assists in the prediction of high-frequency information, and a residual split attention module to adaptively explore and exploit the global structure information in low-level feature space. In addition, an artifact removal strategy is proposed to reduce excessive artifacts and further boost the performance. By progressively connecting the above modules and incrementally fusing the multi-level intermediate feature maps, the fidelity of high-frequency detail information is improved. Finally, we propose a residual split attention network by stacking several RSAGs for reconstructing high-resolution remote sensing images. Extensive experiment results demonstrate that the proposed approach achieves better quantitative metrics and visual quality than the state-of-the-art approaches.