Dear Author
I was recently trying to contrast your thesis approach, but encountered two difficulties along the way.
FaceRecognization score is too high compared to your paper display (Top-1 90%)
MyWay: For each of the 8000 restored images, I use ten clear images as the galley set (containing the corresponding clear images), use the tool to calculate the distance and show Top1~5.
The recurrence effect of DeblurGanV2 is poor (Shen' Celeba_8000 test set PSNR indicator only 23.06)
MyWay: Resize the 128 resolution of Shen's Celeba_test to 256, resize it to 128 after restoration, and then use gt_128 as an indicator.
I hope to seek your help, and at the same time I hope to accurately and truly show the performance of the predecessor model, so as to make a little contribution to the field of deblurring, thank you!
PS: I'd love to get the relevant code if I can, as I can't seem to find it on github. my mail address is xuyu7834@gmail.com
Dear Author I was recently trying to contrast your thesis approach, but encountered two difficulties along the way.
I hope to seek your help, and at the same time I hope to accurately and truly show the performance of the predecessor model, so as to make a little contribution to the field of deblurring, thank you! PS: I'd love to get the relevant code if I can, as I can't seem to find it on github. my mail address is xuyu7834@gmail.com