SGSR: A Saliency-Guided Image Super-Resolution Network

The human visual system finds salient regions in images and allows the cognitive ability to focus on them. Hence, such salient regions play substantial roles in determining the quality of images. However, the existing super-resolution (SR) methods restore all regions of low-resolution images in the same manner. In this paper, we first propose a saliency-guided image super-resolution (SGSR) network where its restoration ability concentrates on the salient regions in the natural images. For this, we propose a saliency learning scheme using newly computed saliency scores for object regions. Then, by providing the saliency features to the saliency-guided attention (SGA) module and using a novel saliency-weighted loss function, the SGSR maximizes the image quality of salient regions and suppresses the excessive generation of unnecessary structures in backgrounds. To the best of our knowledge, this SGSR is the first attempt to induce discriminatory results guided by saliency in the field of natural image SR.