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View Code? Open in Web Editor NEWDual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
License: MIT License
Dual Residual Networks Leveraging the Potential of Paired Operations for Image Restoration
License: MIT License
I am wondering about the experimental environments
Thank you!
Hi你好,在阅读论文的过程中,我对这句话不是很理解:We also choose the kernel size and dilation rate for each DuRB so that the receptive field of convolution in each DuRB grows its size with L.
我尝试自己计算每一层的感受野,并没有发现有倍数关系。能麻烦您发我一份补充材料吗,下面是我的邮箱:[email protected]
不胜感激。
Hi, thanks for your excellent jobs of restoration of multiple distortions .Have you finished the training code ? Thank you very much
Dear authors, hi.
There are some questions I want to ask you:
Your readme writes the environment is python3.7 + PyTorch 1.2, but when I clone your code, I find it has no different with the preceding version of code, which is python2.7 + PyTorch0.31;
Can you update your code version as you say?
I used the code (trained/test) and BSD_gray you provided. The results: 28.43 / 0.8130(noise level = 30), 26.29 / 0.7297(noise level = 50), 24.99 / 0.6685(noise level = 70). It has a bit gap than yours. Is there anything special about your training? Or is this result acceptable?
Thanks for good work.but I have a question,why you do not use norm layer in DuRN-US network in haze removal and use bacthnorm in rain steak removal?
Thanks for your interesting work! I want to ask about the task of additive Gaussian noise removal. Are the different levels of noise dataset ( i.e., 30, 50, 70) trained in a model together or trained separately for each level?
Thanks for your amazing work, my current work need to use your motion_blur.py, it can work when i use CarDataset,but how can i run my own image for test?
Hi,
Thanks for you inspiring work. It would be great if you could share the pre-trained weights for each removals for us.
Thanks
Why are the image bit depths in the BSD500 gray dataset all 32, rather than 8 (single channel)? Aren't all grayscale denoising true single channel Grayscale?
Looking forward to your reply!
Hi, Dr Liu
Thanks for your great job!
I check the testing codes provided by this work, different tasks use different PSNR computation.
E.g.
(1) Denosing: the average channels of PSNR
(2) Deraining Drop: use the Y channel of images.
Could you please tell me that the reason about these implementations?
Thanks a lot!
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