Comments (5)
The authors of Rain100H and Rain100L have updated the datasets. This paper is based on the original datasets.
For the results on new datasets of Rain100H and Rain100L, please refer to https://github.com/csdwren/RecDerain (RainHeavy* and RainLight*)
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The authors of Rain100H and Rain100L have updated the datasets. This paper is based on the original datasets.
For the results on new datasets of Rain100H and Rain100L, please refer to https://github.com/csdwren/RecDerain (RainHeavy* and RainLight*)
Thanks for your reply!
I have another question.
If I want to get a good deraining result, I should put the three datasets together for trainging, right?
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(1) For the individual training dataset, the model fully trained for Rain100L or RainLight* has the best generalization ability to real rainy images.
(2) As for the PReNet for real images, I put the three datasets together. But Rain1400 dataset has much more images than the other two. So when preprocessing, the stride for extracting training patches from Rain1400 is larger. Overall Rain100H plays the more important role, but it is quite different from real rain streaks. I found PReNet trained after 1 epoch has better generalization ability for real images than the model trained after 100 epoches. That is, PReNet after 100 epoches is overfitted to training dataset. PReNet after 1 epoch can better remove rain streaks, but the deraining images are likely to be over-smoothed with less textures. I do not compare the model with (1). Maybe (1) is better.
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Thanks!
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Related Issues (20)
- rescan pretrained model HOT 2
- 关于递归监督 HOT 10
- 视频数据 HOT 1
- No logs/Rain12 .pth files HOT 3
- running time HOT 3
- how to train only LSTM models in stages 2, 3, 4, 5, 6, and 7 and test these 6 models? HOT 3
- test datasets HOT 8
- About network struture HOT 1
- what is h5py version ? HOT 2
- About the train and test datasets HOT 1
- 关于RainTrainL数据集
- Different stage t output
- no file named train_target.h5 HOT 1
- RainTrainL dataset
- 您好,我想问一下Rain100L和Rain100H的分类标准是什么呢?
- Guānyú cèshì 4 / 5,000 Translation results About testing
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