Underwater vision is crucial for autonomous underwater vehicles (AUVs), and enhancing degraded underwater images in real-time on a resource-constrained AUV is a key challenge due to factors like light absorption and scattering, or the sufficient model computational complexity to resolve such factors. Traditional image enhancement techniques lack adaptability to varying underwater conditions, while learning-based methods, particularly those using convolutional neural networks (CNNs) and generative adversarial networks (GANs), offer more robust solutions but face limitations such as inadequate enhancement, unstable training, or mode collapse. Denoising diffusion probabilistic models (DDPMs) have emerged as a state-of-the-art approach in image-to-image tasks but require intensive computational complexity to achieve the desired underwater image enhancement (UIE) using the recent UW-DDPM solution. To address these challenges, this paper introduces UW-DiffPhys, a novel physical-based and diffusion-based UIE approach. UW-DiffPhys combines light-computation physical-based UIE network components with a denoising U-Net to replace the computationally intensive distribution transformation U-Net in the existing UW-DDPM framework, reducing complexity while maintaining performance. Additionally, the Denoising Diffusion Implicit Model (DDIM) is employed to accelerate the inference process through non-Markovian sampling. Experimental results demonstrate that UW-DiffPhys achieved a substantial reduction in computational complexity and inference time compared to UW-DDPM, with competitive performance in key metrics such as PSNR, SSIM, UCIQE, and an improvement in the overall underwater image quality UIQM metric. The implementation code can be found at the following repository: https://github.com/bachzz/UW-DiffPhys

翻译：水下视觉对于自主水下航行器（AUV）至关重要。由于光吸收、散射等因素，或解决此类因素所需的足够模型计算复杂度，在资源受限的AUV上实时增强退化的水下图像是一项关键挑战。传统的图像增强技术缺乏对不同水下条件的适应性，而基于学习的方法，特别是那些使用卷积神经网络（CNN）和生成对抗网络（GAN）的方法，提供了更鲁棒的解决方案，但也面临诸如增强不足、训练不稳定或模式崩溃等限制。去噪扩散概率模型（DDPM）已成为图像到图像任务中的一种先进方法，但使用最近的UW-DDPM解决方案实现所需的水下图像增强（UIE）需要密集的计算复杂度。为应对这些挑战，本文提出了UW-DiffPhys，一种新颖的基于物理和扩散的UIE方法。UW-DiffPhys将轻量计算的基于物理的UIE网络组件与一个去噪U-Net相结合，以替代现有UW-DDPM框架中计算密集的分布变换U-Net，从而在保持性能的同时降低复杂度。此外，采用去噪扩散隐式模型（DDIM）通过非马尔可夫采样来加速推理过程。实验结果表明，与UW-DDPM相比，UW-DiffPhys在计算复杂度和推理时间上实现了显著降低，并在PSNR、SSIM、UCIQE等关键指标上具有竞争力，同时在水下图像整体质量UIQM指标上有所提升。实现代码可在以下仓库找到：https://github.com/bachzz/UW-DiffPhys