mlsanityData to 14 Oct 2026

Restormer-Plus for Real World Image Deraining: One State-of-the-Art Solution to the GT-RAIN Challenge (CVPR 2023 UG2+ Track 3)

arXiv 2305.05454 ↗First posted 9 May 2023cs.CVcited by 2

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Abstract (arXiv)

This technical report presents our Restormer-Plus approach, which was submitted to the GT-RAIN Challenge (CVPR 2023 UG2+ Track 3). Details regarding the challenge are available at http://cvpr2023.ug2challenge.org/track3.html. Restormer-Plus outperformed all other submitted solutions in terms of peak signal-to-noise ratio (PSNR), and ranked 4th in terms of structural similarity (SSIM). It was officially evaluated by the competition organizers as a runner-up solution. It consists of four main modules: the single-image de-raining module (Restormer-X), the median filtering module, the weighted averaging module, and the post-processing module. Restormer-X is applied to each rainy image and built on top of Restormer. The median filtering module is used as a median operator for rainy images associated with each scene. The weighted averaging module combines the median filtering results with those of Restormer-X to alleviate overfitting caused by using only Restormer-X. Finally, the post-processing module is utilized to improve the brightness restoration. These modules make Restormer-Plus one of the state-of-the-art solutions for the GT-RAIN Challenge. Our code can be found at https://github.com/ZJLAB-AMMI/Restormer-Plus.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 5 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

3 of 5 references.

References · 3

  1. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  2. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  3. 2022Not Just Streaks: Towards Ground Truth for Single Image Derainingcited by 31

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.