TrainingCodes: training demo for Gaussian denoising.
demos: Demo_test_DnCNN-.m.
model: including the trained models for Gaussian denoising; a single model for Gaussian denoising, single image super-resolution (SISR) and deblocking.
testsets: BSD68 and Set10 for Gaussian denoising evaluation; Set5, Set14, BSD100 and Urban100 datasets for SISR evaluation; Classic5 and LIVE1 for JPEG image deblocking evaluation.
To run the testing demos Demo_test_DnCNN-.m, you should first install MatConvNet.
Note: If you did not install MatConvNet, just replace res = vl_simplenn(net,input,[],[],'conserveMemory',true,'mode','test') with res = simplenn_matlab(net, input).
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.
| Dataset | Noise Level | BM3D | TNRD | DnCNN-3 |
|---|---|---|---|---|
| 15 | 31.08 / 0.8722 | 31.42 / 0.8826 | 31.46 / 0.8826 | |
| BSD68 | 25 | 28.57 / 0.8017 | 28.92 / 0.8157 | 29.02 / 0.8190 |
| 50 | 25.62 / 0.6869 | 25.97 / 0.7029 | 26.10 / 0.7076 |
| Dataset | Upscaling Factor | TNRD | VDSR | DnCNN-3 |
|---|---|---|---|---|
| 2 | 36.86 / 0.9556 | 37.56 / 0.9591 | 37.58 / 0.9590 | |
| Set5 | 3 | 33.18 / 0.9152 | 33.67 / 0.9220 | 33.75 / 0.9222 |
| 4 | 30.85 / 0.8732 | 31.35 / 0.8845 | 31.40 / 0.8845 | |
| 2 | 32.51 / 0.9069 | 33.02 / 0.9128 | 33.03 / 0.9128 | |
| Set14 | 3 | 29.43 / 0.8232 | 29.77 / 0.8318 | 29.81 / 0.8321 |
| 4 | 27.66 / 0.7563 | 27.99 / 0.7659 | 28.04 / 0.7672 | |
| 2 | 31.40 / 0.8878 | 31.89 / 0.8961 | 31.90 / 0.8961 | |
| BSD100 | 3 | 28.50 / 0.7881 | 28.82 / 0.7980 | 28.85 / 0.7981 |
| 4 | 27.00 / 0.7140 | 27.28 / 0.7256 | 27.29 / 0.7253 | |
| 2 | 29.70 / 0.8994 | 30.76 / 0.9143 | 30.74 / 0.9139 | |
| Urban100 | 3 | 26.42 / 0.8076 | 27.13 / 0.8283 | 27.15 / 0.8276 |
| 4 | 24.61 / 0.7291 | 25.17 / 0.7528 | 25.20 / 0.7521 |
| Dataset | Quality Factor | AR-CNN | TNRD | DnCNN-3 |
|---|---|---|---|---|
| Classic5 | 10 | 29.03 / 0.7929 | 29.28 / 0.7992 | 29.40 / 0.8026 |
| 20 | 31.15 / 0.8517 | 31.47 / 0.8576 | 31.63 / 0.8610 | |
| 30 | 32.51 / 0.8806 | 32.78 / 0.8837 | 32.91 / 0.8861 | |
| 40 | 33.34 / 0.8953 | - | 33.77 / 0.9003 | |
| LIVE1 | 10 | 28.96 / 0.8076 | 29.15 / 0.8111 | 29.19 / 0.8123 |
| 20 | 31.29 / 0.8733 | 31.46 / 0.8769 | 31.59 / 0.8802 | |
| 30 | 32.67 / 0.9043 | 32.84 / 0.9059 | 32.98 / 0.9090 | |
| 40 | 33.63 / 0.9198 | - | 33.96 / 0.9247 |
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