mlsanityData to 14 Oct 2026

End-To-End Underwater Video Enhancement: Dataset and Model

arXiv 2403.11506 ↗First posted 18 Mar 2024cs.CV cs.AIcited by 5

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Abstract (arXiv)

Underwater video enhancement (UVE) aims to improve the visibility and frame quality of underwater videos, which has significant implications for marine research and exploration. However, existing methods primarily focus on developing image enhancement algorithms to enhance each frame independently. There is a lack of supervised datasets and models specifically tailored for UVE tasks. To fill this gap, we construct the Synthetic Underwater Video Enhancement (SUVE) dataset, comprising 840 diverse underwater-style videos paired with ground-truth reference videos. Based on this dataset, we train a novel underwater video enhancement model, UVENet, which utilizes inter-frame relationships to achieve better enhancement performance. Through extensive experiments on both synthetic and real underwater videos, we demonstrate the effectiveness of our approach. This study represents the first comprehensive exploration of UVE to our knowledge. The code is available at https://anonymous.4open.science/r/UVENet.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 29 of its 54 references that are in the map. First posted on arXiv 18 Mar 2024.

Builds on

29 of 54 references.

References · 29

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17 more
  1. 2022VRT: A Video Restoration Transformercited by 220
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.