UNDERWATER IMAGE ENHANCEMENT BASED ON CONDITIONAL DDPM

Abstract

Underwater imaging is often affected by light attenuation and scattering in water, leading to degraded visual quality, such as color distortion, reduced contrast, and noise. Existing underwater image enhancement (UIE) methods, such as Contrast Limited Adaptive Histogram Equalization (CLAHE), Dark Channel Prior (DCP), and Maximum Intensity Projection (MIP), have shown some success but often lack generalization capabilities, making them unable to adapt to various underwater images captured in different aquatic environments and lighting conditions. To address these challenges, a UIE method based on the conditional denoising diffusion probabilistic model (DDPM) is proposed, called DiffWater, which leverages the advantages of DDPM and trains a stable and well-converged model capable of generating high-quality and diverse samples. While methods like CLAHE improve contrast and DCP helps recover depth information by reducing haze, they may not handle all distortion issues in underwater imaging. Therefore, DiffWater introduces a color compensation method that performs channel-wise compensation in the RGB color space, tailored to different water conditions and lighting scenarios. This compensation guides the denoising process, ensuring high-quality restoration of degraded underwater images. Additionally, methods like the Rayleigh Distribution (RAY), Retinex-based Global and Local Image Enhancement (RGHS), and Unsharp Masking with Laplacian Pyramid (ULAP) have been explored to handle noise reduction, contrast enhancement, and edge sharpening, but these methods often struggle with varying lighting conditions and water environments. In DiffWater, the integration of such principles, combined with the conditional guidance provided by the degraded underwater image with color compensation, offers a more adaptive and robust approach. The experimental results show that DiffWater, when tested against existing methods including DCP, RGHS, and ULAP, on four real underwater image datasets, outperforms these comparison methods in terms of enhancement quality and effectiveness. DiffWater exhibits stronger generalization capabilities and robustness, addressing the complex visual distortions present in various underwater conditions more effectively than traditional algorithms like CLAHE, DCP, and MIP.