CISPD: Complementary Illumination–Semantic Prompt Diffusion for Low-Light Remote Sensing Image Enhancement

When performing nighttime passive visible remote sensing of non-emissive land surfaces, illumination is typically dominated by weak moonlight that varies with lunar phase, producing low-radiance images with degraded textures and thus motivating low-radiance visible remote sensing image enhancement. We propose a Complementary Illumination–Semantic Prompt Diffusion framework (CISPD) that incorporates a semantic-invariant prompt and a self-learned illumination-aware prompt to guide diffusion-based low-light remote sensing image enhancement. During denoising, we sequentially inject two complementary prompts. We first retrieve a self-learned illumination-aware prompt from a learnable pool conditioned on the current latent context to correct non-uniform brightness, and then apply a semantic-invariant prompt extracted from a vision foundation model to reinforce geometric structures and suppress artifacts. To keep the two prompts complementary rather than redundant, we introduce a contrastive constraint that encourages their representations to remain distinct, and the dual prompts jointly steer the diffusion trajectory toward well-exposed results with faithful structures. Experiments on iSAID-dark and darkrs, together with LOLv1 and LOLv2, demonstrate that CISPD achieves the best PSNR and SSIM on iSAID-dark, strong qualitative generalization on darkrs, and competitive quantitative performance on LOLv1 and LOLv2.