Taming a Diffusion Model to Revitalize Remote Sensing Image Super-Resolution

Conventional neural network-based approaches for single remote sensing image super-resolution (SRSISR) have made remarkable progress. However, the super-resolution outputs produced by these methods often fall short in terms of visual quality. Recent advances in diffusion models for image generation have demonstrated remarkable potential for enhancing the visual content of super-resolved images. Despite this promise, existing large diffusion models are predominantly trained on natural images, which have huge differences in data distribution, making them hard to apply in remote sensing images (RSIs). This disparity poses challenges for directly applying these models to RSIs. Moreover, while diffusion models possess powerful generative capabilities, their output must be carefully controlled to generate accurate details as the objects in RSIs are small and blurry. In this paper, we introduce RSDiffSR, a novel SRSISR method based on a conditional diffusion model. This framework ensures the high-quality super-resolution of RSIs through three key contributions. First, it leverages a large diffusion model as a generative prior, which substantially enhances the visual quality of super-resolved RSIs. Second, it incorporates low-rank adaptation into the diffusion UNet and multi-stage training process to address the domain gap caused by differences in data distributions. Third, an enhanced control mechanism is designed to process the content and edge information of RSIs, providing effective guidance during the diffusion process. Experimental results demonstrate that the proposed RSDiffSR achieves state-of-the-art performance in both quantitative and qualitative evaluations across multiple benchmarks.