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SD-DCRN: SALIENCY DRIVEN DOUBLE-CHANNEL RESIDUAL NETWORK FOR SUPER-RESOLUTION OF REMOTE SENSING IMAGES

Traditional methods of super-resolution of remote sensing images generally ignore the fact that significant areas usually have a higher demand for super-resolution compared to nonsignificant areas. According to this feature of remote sensing images, we propose a new model of super-resolutio-n based on double-channel residual dense network driven by saliency analysis. Firstly, we use a cascaded partial decoder model to obtain the saliency image of remote sensing images which contributes to distinguishing significant areas and background areas. Secondly, we adopt different super-resolution strategies for regions with different salient values and texture complexity. For the non-salient regions, we adopt the smaller number of RDBs and their internal convolution layers to save computer resources. For the salient regions, we increase the number of RDBs and layers to extract more complex features for super-resolution of the salient regions, which is conducive to the reconstruction of complex texture. Finally, the reconstructed salient regions and non-salient regions are superimposed to obtain the complete super-resolution results. Our experimental results show that the comprehensive perform of our method outperforms other super-resolution models in terms of metrics based on the peak signal-to-noise ratio and structural similarity.