Gradient Enhanced Dual Regression Network: Perception-Preserving Super-Resolution for Multi-Sensor Remote Sensing Imagery

Most existing learning-based single image super-resolution (SISR) methods mainly focus on improving reconstruction accuracy, but they always generate overly smoothed results that fail to match the visual perception. Although perceptual quality can be greatly improved via introducing adversarial loss, image fidelity may decrease to some extent. Moreover, most methods are trained and evaluated on simulated datasets and their performance would drop significantly on real remote sensing imagery. To solve the above problems, we propose a new SISR algorithm named gradient enhanced dual regression network (GEDRN). Based on the dual regression framework, we use share-source residual structure and non-local operation to learn abundant low-frequency information and long-distance spatial correlations. Besides, we not only introduce additional gradient information to avoid blurry results but also apply gradient loss and perceptual loss to further improve the perceptual quality. Our GEDRN is trained and tested on real-world multi-sensor satellite images. Experimental results demonstrate the superiority of the proposed method in achieving much better perceptual quality and ensuring high fidelity.