DCGF: Diffusion-Color-Guided Framework for Underwater Image Enhancement
Yuhan Zhang, Jieyu Yuan, Zhanchuan Cai
Abstract
Underwater exploration is crucial for geoscience and remote sensing, but the capture of underwater images is compromised by the degradation of light absorption and scattering. This article proposes a diffusion-color-guided framework (DCGF) to enhance the quality of underwater images and address color deviations caused by randomness in general diffusion models during underwater image restoration. In DCGF, the diffusion model reconstructs the image distribution, while a color correction module ensures accurate color representation. A conditional image guides the denoising procedure, aligning the diffusion trajectory closely with the target domain. This approach reduces the impact of diffusion variability and minimizes deviations. Once a predetermined denoising threshold is reached, the color correction module extracts salient characteristics of color distribution from luminance and RGB channels, enhancing overall efficacy. The experimental results demonstrate that the DCGF algorithm effectively restores degraded underwater images with robustness and effectiveness. The method successfully corrects color degradation and recovers details in low-light conditions, significantly improving underwater image quality.