A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3

TL;DR

This paper presents a two-stage deraining framework combining low-rank video deraining and transformer-based single image deraining, achieving top performance in the CVPR 2023 UG$^{2}$+ Challenge.

Contribution

The novel two-stage approach effectively leverages multi-frame information and transformer networks, improving deraining quality on real rainy sequences.

Findings

Ranked 1st in SSIM and 2nd in PSNR in the challenge

Effective handling of heavy rain and foggy sequences

Demonstrated superior deraining performance on real datasets

Abstract

In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ 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/UG2_Deraining.