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

• URL: http://arxiv.org/abs/2512.14760v1
• Date: Mon, 15 Dec 2025 18:05:37 GMT
• Title: AquaDiff: Diffusion-Based Underwater Image Enhancement for Addressing Color Distortion
• Authors: Afrah Shaahid, Muzammil Behzad,
• Abstract summary: AquaDiff is a diffusion-based underwater image enhancement framework designed to correct chromatic distortions while preserving structural and perceptual fidelity.<n>A novel cross-domain consistency loss jointly enforces pixel-level accuracy, perceptual similarity, structural integrity, and frequency-domain fidelity.<n>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.
• Score: 0.3437656066916039
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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