# A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG2+ Track 3

arXiv: https://arxiv.org/abs/2305.07979 · HTML: https://mlsanity.com/paper/2305.07979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 4 of 4)

- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Not Just Streaks: Towards Ground Truth for Single Image Deraining](https://mlsanity.com/paper/2206.10779/) (2022)

## Built on by (most cited first, 2 of 2)

- [GT-Rain Single Image Deraining Challenge Report](https://mlsanity.com/paper/2403.12327/) (2024)
- [A Two-Stage Adverse Weather Semantic Segmentation Method for WeatherProof Challenge CVPR 2024 Workshop UG2+](https://mlsanity.com/paper/2406.05513/) (2024)

## Abstract (arXiv)

In this technical report, we briefly introduce the solution of our team HUSTli VIE for GT-Rain Challenge in CVPR 2023 UG2+ Track 3. In this task, we propose an efficient two-stage framework to reconstruct a clear image from rainy frames. Firstly, a low-rank based video deraining method is utilized to generate pseudo GT, which fully takes the advantage of multi and aligned rainy frames. Secondly, a transformer-based single image deraining network Uformer is implemented to pre-train on large real rain dataset and then fine-tuned on pseudo GT to further improve image restoration. Moreover, in terms of visual pleasing effect, a comprehensive image processor module is utilized at the end of pipeline. Our overall framework is elaborately designed and able to handle both heavy rainy and foggy sequences provided in the final testing phase. Finally, we rank 1st on the average structural similarity (SSIM) and rank 2nd on the average peak signal-to-noise ratio (PSNR). Our code is available at https://github.com/yunguo224/UG2Deraining.