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.
Abstract (translated)
水下图像由于波长依赖的光吸收和散射而严重退化，导致颜色失真、对比度低以及细节丢失，这阻碍了基于视觉的水下应用。为了解决这些问题，我们提出了AquaDiff，这是一种基于扩散的水下图像增强框架，旨在纠正色差同时保持结构和感知保真度。AquaDiff结合了一种由色度先验指导的颜色补偿策略与条件扩散过程，在每个去噪步骤中通过交叉注意力动态融合退化输入和噪声潜在状态。增强型去噪骨干网络采用了残差密集块和多分辨率注意机制，捕捉全局颜色背景和局部细节。此外，一种新颖的跨域一致性损失共同确保了像素级准确性、感知相似性、结构完整性和频域保真度。在多个具有挑战性的水下基准测试中的广泛实验表明，AquaDiff与最先进的传统方法、基于CNN的方法、GAN（生成对抗网络）和扩散模型相比，在各种水下条件下提供了出色的颜色校正和竞争性的整体图像质量。
URL
https://arxiv.org/abs/2512.14760
