Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)
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README.md

DnCNN

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Training

Testing

  • [demos] Demo_test_DnCNN-.m.

  • [models] 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.

New FDnCNN Models

I have trained new Flexible DnCNN (FDnCNN) models based on FFDNet.

FDnCNN can handle noise level range of [0, 75] via a single model.

Demo_FDnCNN_Gray.m

Demo_FDnCNN_Gray_Clip.m

Demo_FDnCNN_Color.m

Demo_FDnCNN_Color_Clip.m

Network Design Rationale

  • The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a Gaussian distribution.

    • Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level).
  • Predicting the residual can be interpreted as performing one gradient descent inference step at starting point (i.e., noisy image).

    • The parameters in DnCNN are mainly representing the image priors (task-independent), thus it is possible to learn a single model for different tasks.

Results

Gaussian Denoising

The average PSNR(dB) results of different methods on the BSD68 dataset.

Noise Level BM3D WNNM EPLL MLP CSF TNRD DnCNN DnCNN-B FDnCNN
15 31.07 31.37 31.21 - 31.24 31.42 31.73 31.61 31.69
25 28.57 28.83 28.68 28.96 28.74 28.92 29.23 29.16 29.22
50 25.62   25.87   25.67 26.03   -   25.97 26.23 26.23 26.27

Visual Results

The left is the noisy image corrupted by AWGN, the right is the denoised image by DnCNN.

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.

Gaussian Denoising

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

Single Image Super-Resolution

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

JPEG Image Deblocking

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

Visual Results

The left is the input image corrupted by different degradations, the right is the restored image by DnCNN-3.

Requirements and Dependencies

or just MATLAB R2015b to test the model. https://github.com/cszn/DnCNN/blob/4a4b5b8bcac5a5ac23433874d4362329b25522ba/Demo_test_DnCNN.m#L64-L65

Citation

@article{zhang2017beyond,
  title={Beyond a {Gaussian} denoiser: Residual learning of deep {CNN} for image denoising},
  author={Zhang, Kai and Zuo, Wangmeng and Chen, Yunjin and Meng, Deyu and Zhang, Lei},
  journal={IEEE Transactions on Image Processing},
  year={2017},
  volume={26}, 
  number={7}, 
  pages={3142-3155}, 
}