Abstract
Underwater video pairs are fairly difficult to obtain due to the complex underwater imaging. In this case, most existing video underwater enhancement methods are performed by directly applying the single-image enhancement model frame by frame, but a natural issue is lacking temporal consistency. To relieve the problem, we rethink the temporal manifold inherent in natural videos and observe a temporal consistency prior in dynamic scenes from the local temporal frequency perspective. Building upon the specific prior and no paired-data condition, we propose an implicit representation manner for enhanced video signals, which is conducted in the wavelet-based temporal consistency field, WaterWave. Specifically, under the constraints of the prior, we progressively filter and attenuate the inconsistent components while preserving motion details and scenes, achieving a natural-flowing video. Furthermore, to represent temporal frequency bands more accurately, an underwater flow correction module is designed to rectify estimated flows considering the transmission in underwater scenes. Extensive experiments demonstrate that WaterWave significantly enhances the quality of videos generated using single-image underwater enhancements. Additionally, our method demonstrates high potential in downstream underwater tracking tasks, such as UOSTrack and MAT, outperforming the original video by a large margin, i.e., 19.7% and 9.7% on precise respectively.
Abstract (translated)
由于水下成像的复杂性，获取高质量的水下视频配对数据相当困难。在这种情况下，大多数现有的水下视频增强方法都是通过直接在每一帧上应用单图像增强模型来实现的，但这样会产生缺乏时间一致性的问题。为了解决这个问题，我们重新审视了自然视频中固有的时间流形，并从局部时间频率的角度观察到了动态场景中的时间一致性的先验知识。基于这种特定的先验和未配对数据的情况，我们提出了一种在基于小波的时间一致性场（WaterWave）内进行增强视频信号隐式表示的方法。具体来说，在遵循先验约束的情况下，我们逐步过滤并削弱不一致的成分，同时保留运动细节和场景，从而生成自然流畅的视频。 为了更准确地表示时间频率带，设计了一个水下流修正模块来纠正估计的流动情况，考虑了水下场景中的传输特性。大量的实验表明，WaterWave显著提高了使用单图像增强方法生成的视频质量。此外，我们的方法在下游的水下跟踪任务（如UOSTrack和MAT）中表现出很高的潜力，并且大幅优于原始视频，在精度上分别高出19.7%和9.7%。
URL
https://arxiv.org/abs/2512.05492
