Coupled Adversarial Training for Remote Sensing Image Super-Resolution

Generative adversarial network (GAN) has made great progress in recent natural image super-resolution tasks. The key to its success is the integration of a discriminator which is trained to classify whether the input is a real high-resolution (HR) image or a generated one. Arguably, learning a strong discriminative prior is essential for generating high-quality images. However, in remote sensing images, we discover, through extensive statistical analysis, that there are more low-frequency components than natural images, which may lead to a "discrimination-ambiguity" problem, i.e., the discriminator will become "confused" to tell whether its input is real or not when dealing with those low-frequency regions, and therefore, the quality of generated HR images may be deeply affected. To address this problem, we propose a novel GAN-based super-resolution algorithm named coupled-discriminated GANs (CDGANs) for remote sensing images. Different from the previous GAN-based super-resolution models in which their discriminator takes in a single image at one time, in our model, the discriminator is specifically designed to take in a pair of images: a generated image and its HR ground truth, to make better discrimination of the inputs. We further introduce a dual pathway network architecture, a random gate, and a coupled adversarial loss to learn better correspondence between the discriminative results and the paired inputs. Experimental results on two public data sets demonstrate that our model can obtain more accurate super-resolution results in terms of both visual appearance and local details compared with other state of the arts. Our code will be made publicly available.