NLRN_v0
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Code of Non-Local Recurrent Network for Image Restoration (NeurIPS 2018)
I just follow default setting to retrain the model and then test it, but the output image seems like bellow. Test the image with the pretrained model given in [https://drive.google.com/open?id=1l_G9wniOKSM4dS8NqGP-SptPVT5PFo6l](sigma25)...
When training, it only do evalute and give PSNR when save chackpoints. change here is useless: parser.add_argument( '--eval-steps', help='Number of steps to run evaluation for at each checkpoint', default=100, type=int)...
Hi, What's the defference between new version and the older version?
Hi i run your command provided in readme of new version for 500k step but the psnr reached only to 28 ,far away from your metrics (30.8) in your paper...
Sorry to bother, the link of pretrained model has been disabled could you provide a new link? Thanks a lot.
https://github.com/Ding-Liu/NLRN/blob/ca5c0a54329c157b31f2daf708e20f677546b208/models/nlrn.py#L218 Why not use the "else" branch to train the model without crop patches?
I find that the output of this non-local module on each location only depends on its q × q neighborhood, as descripted in your paper, but I can not find...