Low-Light Remote Sensing Image Enhancement via Priors Guided End-to-End Latent Residual Diffusion

Low-light image enhancement, especially for remote sensing images, remains a challenging task due to issues like low brightness, high noise, color distortion, and the unique complexities of remote sensing scenes, such as uneven illumination and large coverage. Existing methods often struggle to balance efficiency, accuracy, and robustness. Diffusion models have shown potential in image restoration, but they often rely on multi-step noise estimation, leading to inefficiency. To address these issues, this study proposes an enhancement framework based on a lightweight encoder–decoder and a physical-prior-guided end-to-end single-step residual diffusion model. The lightweight encoder–decoder, tailored for low-light scenarios, reduces computational redundancy while preserving key features, ensuring efficient mapping between pixel and latent spaces. Guided by physical priors, the end-to-end trained single-step residual diffusion model simplifies the process by eliminating multi-step noise estimation through end-to-end training, accelerating inference without sacrificing quality. Illumination-invariant priors guide the inference process, alleviating blurriness from missing details and ensuring structural consistency. Experimental results show that it not only demonstrates superiority over mainstream methods in quantitative metrics and visual effects but also achieves a 20× speedup compared with an advanced diffusion-based method.