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View Code? Open in Web Editor NEWSyn2Real Transfer Learning for Image Deraining using Gaussian Processes
License: MIT License
Syn2Real Transfer Learning for Image Deraining using Gaussian Processes
License: MIT License
Thank you very much for your answer to my previous question about the real dataset.
Now I am confused by the non-reffereced metrics of NIQE and BRISQUE.
I test them with the code found online and find the results are quite different with those in your paper, even for the input real rainy images. The reason may be the uncorrect or different implementations of computing them.
So I am wondering if you can release the test code or provide an accesible link of the calculation of these two metrics.
Thanks again for your kindness.
where is this file? I cannot fint it. thanks!
I am very happy that you have made so many contributions. Can you provide all the data for the test after the rain with the synthetic rain image and the real rain image?
Hello, Thanks for sharing this wonderful work.
I have a question about the testing data.
Are the images in the real-world test set from DDN-SIRR the same as those utilized in the training phase?
Thank you very much.
I think the data of 'norain' that you provided is not correct, could you please provide the corresponding dataset? Thanks.
Hiiiiii, thanks for ur great job! i encounterd with the issue that the training time is too long(about 10h an epoch in deblur task) and i want to konw how can i speed up the training period. BTW, i want to konw how long about ur training time.
Looking forward for ur reply! thanks in advance!
Hi,
Thanks for your contributions to the deraining research area. After going through the details of your work, I have a question about your algorithm. I notice that your real-world test dataset is the same as the real-world train dataset. My question is if I give you a new real-world rainy dataset, do you need to retrain the network? Or you can directly predict the results.
Looking forward to hearing from you.
Thanks.
, so can you send a download link summarizing DIDMDN , Rain800, JORDER_200L, DDN ?
mention test dataset text file in the line 57 of test.py, for example
i do not know this 'mention' means what ?
Hi, I am curious about the experimental dataset.
You used 9,100 train images in DDN when you did real experiments.
Did you use the same 9,100 sheets for cross domain experiment? Or did you use 12,600 sheets as an official dataset?
I wonder if the Rain200H and Rain800 also used all of the training sets for cross domain experiment.
Hi, I train the model with
the 9100 pairs of images in the original dataset of Fu et al. [9] as the labeled image, the 147 images in real_input_split1.txt as the unlabeled image and the SIRR_test.txt as the val_filename;
but when I test the trained model on pictures in data/test/derain/SIRR_test, there are still some obvious white rain streaks on the SIRR_test, and the visual quality of test using retrained model (val_ssim is about 0.86) is much worser than using the DDN_SIRR_with_GP derain_best provided in the project(val_ssim is about 0.91).
Thank you very much!
Where can i find the result pictures after deraining?
Hi, according to the paper, for the dataset DDN-SIRR, 9100 pairs of synthesized images and some real-world images are used as the training set, and another 20 pairs as testing set. I have some questions as follows:
Thank you very much!
I think the data of 'norain' that you provided is not correct, could you please provide the corresponding dataset? Thanks.
Can you provide the DID-Data、DDN-Data and SPA-Data on psnr/ssim result?
Traceback (most recent call last):
File "/home/zhao/lianxi/py1/S2/Syn2Real-master/test.py", line 77, in
os.mkdir('./{}_results/{}/'.format(category,exp_name))
FileNotFoundError: [Errno 2] No such file or directory: './derain_results/DDN_SIRR_withGP/'
Thanks for ur great job!
In the deblur task, i want to know what is the gt of the unlabeled sample during the unlabeled phase, i.e., in line 232th of "train.py".
Hope for your reply! Thanks in advance.
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