# Marine Snow Removal Using Internally Generated Pseudo Ground Truth

arXiv: https://arxiv.org/abs/2504.19289 · HTML: https://mlsanity.com/paper/2504.19289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [BVI-RLV: A Fully Registered Dataset for Low-Light Video Enhancement](https://mlsanity.com/paper/2407.03535/) (2024)
- [Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement](https://mlsanity.com/paper/2501.14265/) (2025)
- [A deep learning approach for marine snow synthesis and removal](https://mlsanity.com/paper/2311.15584/) (2023)

## Abstract (arXiv)

Underwater videos often suffer from degraded quality due to light absorption, scattering, and various noise sources. Among these, marine snow, which is suspended organic particles appearing as bright spots or noise, significantly impacts machine vision tasks, particularly those involving feature matching. Existing methods for removing marine snow are ineffective due to the lack of paired training data. To address this challenge, this paper proposes a novel enhancement framework that introduces a new approach for generating paired datasets from raw underwater videos. The resulting dataset consists of paired images of generated snowy and snow, free underwater videos, enabling supervised training for video enhancement. We describe the dataset creation process, highlight its key characteristics, and demonstrate its effectiveness in enhancing underwater image restoration in the absence of ground truth.