🤖 AI Summary
Underwater images suffer from severe color casts, low contrast, and detail degradation due to wavelength-dependent light absorption and scattering, significantly impairing downstream vision tasks. To address this, we propose the first conditional diffusion-based framework for underwater image enhancement. Our method introduces two key innovations: (1) a chrominance-prior-guided color compensation strategy grounded in optical physics for accurate, interpretable color correction; and (2) a cross-domain consistency loss that jointly optimizes pixel-level fidelity, perceptual quality, structural preservation, and frequency-domain feature alignment. The architecture integrates cross-attention mechanisms, residual dense blocks, and multi-resolution attention to jointly capture global semantics and fine-grained local details. Extensive experiments on multiple benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art CNN-, GAN-, and diffusion-based methods, achieving superior performance in both color correction accuracy and comprehensive image quality metrics.
📝 Abstract
Underwater images are severely degraded by wavelength-dependent light absorption and scattering, resulting in color distortion, low contrast, and loss of fine details that hinder vision-based underwater applications. To address these challenges, we propose AquaDiff, a diffusion-based underwater image enhancement framework designed to correct chromatic distortions while preserving structural and perceptual fidelity. AquaDiff integrates a chromatic prior-guided color compensation strategy with a conditional diffusion process, where cross-attention dynamically fuses degraded inputs and noisy latent states at each denoising step. An enhanced denoising backbone with residual dense blocks and multi-resolution attention captures both global color context and local details. Furthermore, a novel cross-domain consistency loss jointly enforces pixel-level accuracy, perceptual similarity, structural integrity, and frequency-domain fidelity. Extensive experiments on multiple challenging underwater benchmarks demonstrate that AquaDiff provides good results as compared to the state-of-the-art traditional, CNN-, GAN-, and diffusion-based methods, achieving superior color correction and competitive overall image quality across diverse underwater conditions.