1 Prior: pixeldistribution features
In lowlevel vision problems, pixellevel features are the most important features. We compare the histograms of different images in various noise levels to investigate the pixellevel features having a certain of consistency. As we can see from Fig. 1 and Fig. 2, the pixeldistribution in noisy images is more similar in higher noise level than lower noise level .
WIN inferences noisefree images based on the learned pixeldistribution features. When the noise level is the higher, the pixeldistribution features are more similar. Thus, WIN can learn more pixeldistribution features from noisy images having higher level noise. This is the reason that WIN performs even better in higherlevel noise, which can be seen and verified in section 3.
2 Wider Convolution Inference Strategy
In Fig. 3, we illustrate the architectures of WIN5, WIN5R, and WIN5RB.
2.1 Architectures
Three proposed models have the identical basic structure: layers and filters of size in most convolution layers, except for the last one with filter of size
. The differences among them are whether batch normalization (BN) and an inputtooutput skip connection are involved. WIN5RB has two types of layers with two different colors. (a) Conv+BN+ReLU
nair2010rectified : for layers 1 to , BN is added between Conv and ReLU nair2010rectified . (b) Conv+BN: for the last layer, filters of size is used to reconstruct the . In addition, a shortcut skip connecting the input (data layer) with the output (last layer) is added to merge the input data with as the final recovered image.2.2 Having Knowledge Base with BatchNormal
In this work,
we employ Batch Normalization (BN) for extracting pixeldistribution statistic features and reserving training data means and variances in networks for denoising inference instead of using the regularizing effect of improving the generalization of a learned model.
The regularizerBN can keep the data distribution the same as input: Gaussian distribution. This distribution consistency between input and regularizer ensures more pixeldistribution statistic features can be extracted accurately. The integration of BN
ioffe2015batch into more filters will further preserve the prior information of the training set. Actually, a number of stateoftheart studies elad2006image ; joshi2009image ; xu2015patch have adopted image priors (e.g. distribution statistic information) to achieve impressive performance.Can Batch Normalization work without a Skip Connection? In WINs, BN ioffe2015batch cannot work without the inputtooutput skip connection and is always overfitting. In WIN5RB’s training, BN keeps the distribution of input data consistent and the skip connection can not only introduce residual learning but also guide the network to extract the certain features in common: pixeldistribution. Without the input data as a comparison, BN could bring negative effects by keeping the each input distribution same, especially, when a task is to output pixellevel feature map. In DnCNN, two BN layers are removed from the first and last layers, by which a certain degree of the BN’s negative effects can be reduced. Meantime DnCNN also highlights network’s generalization ability largely relies on the depth of networks.
In Fig.4, learned priors (means and variances) are preserved in WINs as knowledge base for denoising inference. When WIN has more channels to preserve more data means and variances, various combinations of these feature maps can corporate with residual learning to infer the noisefree images more accurately.
3 Experimental Results
PSNR (dB) / SSIM  

BM3D dabov2009bm3d  REDNet mao2016image  DnCNN zhang2016beyond  WIN5  WIN5R  WIN5RBS  WIN5RBB  
10  34.02/0.9182  32.96/0.8963  34.60/0.9283  34.10/0.9205  34.43/0.9243  35.83/0.9494  35.43/0.9461 
30  28.57/0.7823  29.05/0.8049  29.13/0.8060  28.93/0.7987  30.94/0.8644  33.62/0.9193  33.27/0.9263 
50  26.44/0.7028  26.88/0.7230  26.99/0.7289  28.57/0.7979  29.38/0.8251  31.79/0.8831  32.18/0.9136 
70  25.23/0.6522  26.66/0.7108  25.65/0.6709  27.98/0.7875  28.16/0.7784  30.34/0.8362  31.07/0.8962 
Run Time(s)  
30  1.67  69.25  13.61  15.36  15.78  20.39  15.82 
50  2.87  70.34  13.76  16.70  22.72  21.79  13.79 
70  2.93  69.99  12.88  16.10  19.28  20.86  13.17 
3.1 Quantitative Result
The quantitative result on test set BSD200 is shown on Table 1 including noise levels . Moreover, we compare WIN5RBB, DnCNN and BM3D behaviors at different noise levels of average PSNR on BSD200test. As we can see from Fig.5, WIN5RBB (blind denoising) trained for outperforms BM3D dabov2009bm3d and DnCNN zhang2016beyond on all noise levels and is significantly more stable even on higher noise levels.
In addition, in Fig.5, as the noise level is increasing, the performance gain of WIN5RBB is getting larger, while the performance gain of DnCNN comparing to BM3D is not changing much as the noise level is changing. Compared with WINs, DnCNN is composed of even more layers embedded with BN. This observation indicates that the performance gain achieved by WIN5RB does not mostly come from BN’s regularization effect but the pixeldistribution features learned and relevant priors such as means and variances reserved in WINs. Both Larger kernels and more channels can promote CNNs more likely to learn pixeldistribution features.
3.2 Visual results
For Visual results, We have various images from two different datasets, BSD200test and Set12, with noise levels applied separately.
One image from BSD200test with noise level=10
One image from BSD200test with noise level=30
One image from BSD200test with noise level=50
One image from BSD200test with noise level=70
One image from Set12 with noise level=10
One image from Set12 with noise level=30
One image from Set12 with noise level=50
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