# Removing Rain in Videos: A Large-scale Database and A Two-stream ConvLSTM Approach

arXiv: https://arxiv.org/abs/1906.02526 · HTML: https://mlsanity.com/paper/1906.02526/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 4 of 4)

- [Triple-level Model Inferred Collaborative Network Architecture for Video Deraining](https://mlsanity.com/paper/2111.04459/) (2021)
- [ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning](https://mlsanity.com/paper/2309.00956/) (2023)
- [Dual-Path Multi-Scale Transformer for High-Quality Image Deraining](https://mlsanity.com/paper/2405.18124/) (2024)
- [Your Pre-trained Diffusion Model Secretly Knows Restoration](https://mlsanity.com/paper/2604.04924/) (2026)

## Abstract (arXiv)

Rain removal has recently attracted increasing research attention, as it is able to enhance the visibility of rain videos. However, the existing learning based rain removal approaches for videos suffer from insufficient training data, especially when applying deep learning to remove rain. In this paper, we establish a large-scale video database for rain removal (LasVR), which consists of 316 rain videos. Then, we observe from our database that there exist the temporal correlation of clean content and similar patterns of rain across video frames. According to these two observations, we propose a two-stream convolutional long- and short- term memory (ConvLSTM) approach for rain removal in videos. The first stream is composed of the subnet for rain detection, while the second stream is the subnet of rain removal that leverages the features from the rain detection subnet. Finally, the experimental results on both synthetic and real rain videos show the proposed approach performs better than other state-of-the-art approaches.