Diffusion Model with Detail Complement for Super-Resolution of Remote Sensing

Abstract

Remote sensing super-resolution (RSSR) aims to improve remote sensing (RS) image resolution while providing finer spatial details, which is of great significance for high-quality RS image interpretation. The traditional RSSR is based on the optimization method, which pays insufficient attention to small targets and lacks the ability of model understanding and detail supplement. To alleviate the above problems, we propose the generative Diffusion Model with Detail Complement (DMDC) for RS super-resolution. Firstly, unlike traditional optimization models with insufficient image understanding, we introduce the diffusion model as a generation model into RSSR tasks and regard low-resolution images as condition information to guide image generation. Next, considering that generative models may not be able to accurately recover specific small objects and complex scenes, we propose the detail supplement task to improve the recovery ability of DMDC. Finally, the strong diversity of the diffusion model makes it possibly inappropriate in RSSR, for this purpose, we come up with joint pixel constraint loss and denoise loss to optimize the direction of inverse diffusion. The extensive qualitative and quantitative experiments demonstrate the superiority of our method in RSSR with small and dense targets. Moreover, the results from direct transfer to different datasets also prove the superior generalization ability of DMDC.