AquaDiff: Diffusion-Based Underwater Image Enhancement for Addressing Color Distortion

TL;DR

AquaDiff is a diffusion-based framework that effectively corrects color distortions and enhances details in underwater images, outperforming existing methods in color accuracy and overall image quality.

Contribution

The paper introduces AquaDiff, a novel diffusion-based underwater image enhancement method combining chromatic priors, cross-attention, and a new loss for superior color correction and detail preservation.

Findings

Outperforms state-of-the-art methods in color correction

Achieves superior image quality across diverse underwater conditions

Demonstrates effectiveness through extensive benchmark experiments

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.