SD-FB-GAN: SALIENCY-DRIVEN FEEDBACK GAN FOR REMOTE SENSING IMAGE SUPER-RESOLUTION RECONSTRUCTION

The visual characteristics of different regions in remote sensing images are significantly versatile, which poses a huge challenge to single image super-resolution. Although generative adversarial network (GAN) has shown great potential in generating photo-realistic results, it provides unsatisfactory performance in objective metrics owning to pseudo textures brought by adversarial learning. In this paper, we propose a new saliency-driven feedback GAN to cope with these problems. We design a saliency-driven feedback generator based on paired-feedback blocks (PFBBs) and recurrent structure to provide strong reconstruction ability. In the PFBB, the saliency map serves as an indicator to reflect the texture complexity, so different reconstruction principles can be applied to restore areas with varying levels of saliency. Besides, we propose to measure the visual quality of salient areas, non-salient areas, and the whole image with multi-discriminators, which can dramatically eliminate pseudo textures. Comprehensive evaluations and ablation studies validate the superiority of our proposal.