Remote sensing images are susceptible to atmospheric scattering, imaging conditions, and post-processing strategies during actual acquisition, resulting in issues such as low contrast, insufficient color saturation, and overall poor visual quality. These problems significantly degrade the color quality and expressiveness of the imagery. To address these issues, a prior-guided conditional diffusion enhancement framework (PGCDE) is proposed in this paper. First, an unconditional diffusion model is built upon large-scale natural images to extract stable color priors. Then, these prior features are dynamically injected into the conditional enhancement network through an adaptive hierarchical feature fusion (AHFF) module, with a multi-domain joint loss introduced during training to constrain structural consistency. Finally, at the inference stage, a luminance-decoupled multi-scale fusion strategy is employed to recombine the generated low-frequency color tones with the high-frequency textures of the original image. Experiments on the GID-5 and LoveDA datasets demonstrate that the proposed method outperforms existing representative approaches, providing a practical solution for remote sensing image color quality enhancement that balances perceptual improvement with structure preservation.
Song et al. (Mon,) studied this question.