• DnCNN-keras

• DnCNN-pytorch

• Simplenn version

  • DnCNN_TrainingCodes_v1.1

• DagNN version

  • DnCNN_TrainingCodes_DagNN_v1.1

• [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.

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 Architecture

  

• Batch normalization and residual learning are beneficial to Gaussian denoising (especially for a single noise level). The residual of a noisy image corrupted by additive white Gaussian noise (AWGN) follows a constant Gaussian distribution which stablizes batch normalization during training.

  • Histogram of noisy patches, clean patches, and residual (noise) patches from a batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128.
  
  • Histogram of noisy patches, clean patches, and residual (noise) patches from another batch of training. The noise level is 25, the patch size is 40x40, the batch size is 128.
  
  • Noise-free image super-resolution does not have this property.

• 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, such as image denoising, image super-resolution and JPEG image deblocking.

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

  

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

Visual Results

The left is the noisy image corrupted by AWGN, the middle is the denoised image by DnCNN, the right is the ground-truth.

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.

• MATLAB R2015b
• Cuda-8.0 & cuDNN v-5.1
• MatConvNet

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

@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}, 
}