A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023
  Workshop UG$^{\textbf{2}}$+ Track 3
Yun Guo Xueyao Xiao Xiaoxiong Wang Yi Li Yi Chang Luxin Yan
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.