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Found 5 papers, 3 papers with code
GT-Rain Single Image Deraining Challenge Report
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023.
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
Learning-based image deraining methods have made great progress.
Mutual Query Network for Multi-Modal Product Image Segmentation
Product image segmentation is vital in e-commerce.
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG$^{\textbf{2}}$+ Track 3
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.
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
In addition, considering that the existing real rain datasets are of low quality, either small scale or downloaded from the internet, we collect a real large-scale dataset under various rainy kinds of weather that contains high-resolution rainy images.
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