用扩散模型修复水下图像色彩失真，保留细节和结构。
AquaDiff: Diffusion-Based Underwater Image Enhancement for Addressing Color Distortion
- 结合颜色先验与条件扩散，动态融合噪声与输入图像
- 在多个水下数据集上实现最优色彩校正效果
- 适合需要高保真水下视觉的科研与应用开发者
水下图像因波长相关的光吸收和散射严重退化，导致色彩失真、对比度低、细节丢失，阻碍基于视觉的水下应用。为解决此问题，我们提出AquaDiff，一种基于扩散的水下图像增强框架，旨在纠正色彩失真的同时保持结构和感知保真度。AquaDiff将色度先验引导的颜色补偿策略与条件扩散过程结合，在去噪每一步通过交叉注意力动态融合退化输入与噪声潜在状态。采用带残差密集块和多分辨率注意力的增强去噪主干网络，捕捉全局色彩上下文与局部细节。此外，设计一种新颖的跨域一致性损失，联合约束像素级精度、感知相似性、结构完整性与频域保真度。在多个具有挑战性的水下基准测试上，AquaDiff优于现有传统方法、CNN、GAN及扩散模型，实现更优的色彩校正与竞争力的整体图像质量。
原文摘要 · Abstract (English)
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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