AquaDiff: Diffusion-Based Underwater Image Enhancement for Addressing Color Distortion
This addresses the challenge of improving vision-based underwater applications by enhancing image quality, though it appears incremental as it builds on existing diffusion methods.
The paper tackled the problem of color distortion and degradation in underwater images by proposing AquaDiff, a diffusion-based enhancement framework, which achieved superior color correction and competitive overall image quality compared to state-of-the-art methods on multiple benchmarks.
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.