UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors

TL;DR

UnDIVE introduces a two-stage underwater video enhancement framework that leverages generative priors and physics-based models to improve visual quality and temporal consistency in real-time, across diverse water conditions.

Contribution

The paper presents a novel two-stage method combining diffusion models and physics-based enhancement for underwater videos, addressing temporal dynamics and data scarcity.

Findings

Outperforms existing methods on four datasets

Enables real-time processing of high-resolution videos

Generalizes well across diverse water types

Abstract

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.