Hi, Longguang! Thanks for your interesting work and sharing your code! As we reproduce your code, the best PSNR results on Set14 as sigma=3.6 is 27.01 dB while what your paper claimed is 27.45 dB! I know your model can perform well as sigma is small. However, as the sigma become large, there is a big performance gap between our reproduced results and your paper giving results. My colleague also reproduce your code many times, but the same problems still exists!
The same problems seems proposed in the https://github.com/The-Learning-And-Vision-Atelier-LAVA/DASR/issues/38.
So can your tell me the error of our training command, shown as below? If there is no problem, for your academic reputation, I hope you reproduce your code again and evaluate your model on 4x Set14! Thanks!
Hi, Longguang! Thanks for your interesting work and sharing your code! As we reproduce your code, the best PSNR results on Set14 as sigma=3.6 is 27.01 dB while what your paper claimed is 27.45 dB! I know your model can perform well as sigma is small. However, as the sigma become large, there is a big performance gap between our reproduced results and your paper giving results. My colleague also reproduce your code many times, but the same problems still exists!
The same problems seems proposed in the https://github.com/The-Learning-And-Vision-Atelier-LAVA/DASR/issues/38. So can your tell me the error of our training command, shown as below? If there is no problem, for your academic reputation, I hope you reproduce your code again and evaluate your model on 4x Set14! Thanks!
training: python main.py --dir_data='/media/datasets' \ --model='blindsr' \ --scale='4' \ --blur_type='iso_gaussian' \ --noise=0.0 \ --sig_min=0.2 \ --sig_max=4.0 \ --sig 3.6 \ --n_GPUs 2 test: python test.py --test_only \ --dir_data='/media/datasets' \ --data_test='Set14' \ --model='blindsr' \ --scale='4' \ --resume=600 \ --n_GPUs=1 \ --save 'test'\ --blur_type='iso_gaussian' \ --sig=3.6