$ python generate_patches.py
$ python main.py
(note: You can add command line arguments according to the source code.)
Results
Gaussian Denoising
The average PSNR(dB) results of different methods on the BSD68 dataset.
Noise Level
BM3D
WNNM
EPLL
MLP
CSF
TNRD
DnCNN-S
DnCNN-B
15
31.07
31.37
31.21
-
31.24
31.42
31.73
31.61
25
28.57
28.83
28.68
28.96
28.74
28.92
29.23
29.16
50
25.62
25.87
25.67
26.03
-
25.97
26.23
26.23
Gaussian Denoising, Single ImageSuper-Resolution and JPEG Image Deblocking via a Single (DnCNN-3) Model
Average PSNR(dB)/SSIM results of different methods for Gaussian denoising with noise level 15, 25 and 50 on BSD68 dataset, single image super-resolution with
upscaling factors 2, 3 and 40 on Set5, Set14, BSD100 and Urban100 datasets, JPEG image deblocking with quality factors 10, 20, 30 and 40 on Classic5 and LIVE11 datasets.