用生成先验提升水下视频画质，兼顾时序一致性和多样性。
UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors
- 分两阶段：先用去噪扩散模型学生成先验，再结合物理模型增强画质。
- 在4个数据集上优于现有方法，支持多种水质和高分辨率实时处理。
- 无需真实参考图，适合缺乏标注的海洋探索场景。
随着海洋探索兴起，水下成像成为研究热点。水下视频增强对实时计算机视觉任务至关重要。然而，现有方法多聚焦单帧增强，忽略视频时序动态，导致视觉效果差；且缺乏真实参考图像，限制了大量可用水下视频数据的应用。为此，我们提出一种两阶段框架：第一阶段使用去噪扩散概率模型从无标签数据中学习生成先验，捕捉鲁棒、描述性强的特征表示；第二阶段将该先验融入基于物理的图像建模中，实现空间增强，并保持帧间时序一致性。本方法可在低分辨率下实现高分辨率水下视频的实时、高效处理，对多种水体类型均有良好泛化能力。在四个数据集上的大量实验表明，该方法性能优于现有技术。代码已开源：github.com/suhas-srinath/undive。
原文摘要 · Abstract (English)
With the rise of marine exploration, underwater imaging has gained significant attention as a research topic. Underwater video enhancement has become crucial for real-time computer vision tasks in marine exploration. However, most existing methods focus on enhancing individual frames and neglect video temporal dynamics, leading to visually poor enhancements. Furthermore, the lack of ground-truth references limits the use of abundant available underwater video data in many applications. To address these issues, we propose a two-stage framework for enhancing underwater videos. The first stage uses a denoising diffusion probabilistic model to learn a generative prior from unlabeled data, capturing robust and descriptive feature representations. In the second stage, this prior is incorporated into a physics-based image formulation for spatial enhancement, while also enforcing temporal consistency between video frames. Our method enables real-time and computationally-efficient processing of high-resolution underwater videos at lower resolutions, and offers efficient enhancement in the presence of diverse water-types. Extensive experiments on four datasets show that our approach generalizes well and outperforms existing enhancement methods. Our code is available at github.com/suhas-srinath/undive.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。