A Conditional Diffusion Method Based on Cross-Domain Prior Guidance for Color Enhancement of Remote Sensing Images
Year: 2026
Type: article
Abstract: 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.
Source: Remote Sensing
DOI: 10.3390/rs18091339
Authors: Zhengguang Song, Zhijiang Li, Jiahui Song
Institution: Wuhan University
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7156218320
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