Toward Convolutional Blind Denoising of Real Photographs ......Toward Convolutional Blind Denoising...

Toward Convolutional Blind Denoising of Real Photographs:Supplementary material

Shi Guo1,3,4, Zifei Yan(�

) 1, Kai Zhang1,3, Wangmeng Zuo1,2, Lei Zhang3,4

1Harbin Institute of Technology, Harbin; 2Peng Cheng Laboratory, Shenzhen;3 The Hong Kong Polytechnic University, Hong Kong; 4DAMO Academy, Alibaba Group

guoshi28@outlook.com, {wmzuo,yanzifei}@hit.edu.cncskaizhang@gmail.com, cslzhang@comp.polyu.edu.hk

1. DNDWe provide more results (i.e., Figs 3, 4, 5, 6, 7, 8, 9)

containing fine textures on images from the DND dataset.

2. SIDDRecently, a new dataset, Smartphone Image Denoising

Dataset (SIDD) [1], is presented to evaluate the denoisingperformance for smart cameras. Since the online evaluationsystem is not available when we submit this paper, we onlyreport several examples of denoising results for qualitativeevaluation. The visual comparisons are shown in Figs 10,11, 12, 13, 14, 15, 16. One can see that the performanceof our CBDNet is preferable against other blind denoisingalgorithms.

3. More Ablation StudiesFor better justification, we consider three CBDNet vari-

ants, (i) CBDNet(w/o E): remove noise estimation subnetand increase kernel size of the 1st layer to 5 × 5 to makethe parameter numbers equal or greater than CBDNet, (ii)CBDNet(w/o A): set α = 0.5 in asymmetric loss, (ii) CBD-Net(w/o TV): remove TV loss. To assess the generalizationability, we adopt two types of noise, (a) Real noise includingDND and realistic noise in Eqn.2 with Indcrf = 5, pattern= ’gbrg’, σs = 0.04 and σc = 0.02, (b) Salt & Pepper (SP)noise with noise density d = 0.4, 0.8. From Tab. 1, CBD-Net slightly outperforms CBDNet(w/o E) for Eqn.2 noisecontaining in the training data, and is much better (> 1.0dBgain) for SP noise, indicating that noise estimation bene-fits generalization ability. The effect of TV and asymmetriclosses is also given in Tab. 1.

To evaluate the accurate of estimated noise level, wetest two realistic noise level defined in Eqn.2 with (i)Indcrf = 5, pattern = ’gbrg’, σs = 0.08, σc = 0.03 and(ii) Indcrf = 5, pattern = ’gbrg’, σs = 0.10, σc = 0.05on CBSD68 dataset. Quantitative results of CBDNet andCBDNet(w/o TV) are given in Tab. 2. One can see that,comparing with CBDNet(w/o TV), CBDNet utilizing TV

loss estimates more accurate noise level. Furthermore, wechoose one image in CBSD68, synthesize the noisy imageusing the setting Indcrf = 5, pattern = ’gbrg’, σs = 0.08and σc = 0.03 and show the visual results of estimatednoise map. From Fig. 1, we can see that both CBDNet andCBDNet(w/o TV) can reasonably estimate noise level map,but CBDNet(w/o TV) suffers from relatively large fluctua-tions.

4. Effect of Perspective Loss

From Fig. 2, CBDNet w/o perceptual loss is effective inremoving noise but may cause oversmoothing of structureand textures. Using perceptual loss benefits texture preserv-ing and improves the visual quality of denoising result.

Table 1: The quantitative results on different kind of noise.Dataset Noise Type Methods PSNR(dB)

DND [5] Real-world noise CBDNet(w/o E) 37.41CBDNet 37.57

CBSD68

Indcrf =5, pattern = ’gbrg’σs= 0.04,σc= 0.02(Equ.2)

CBDNet(w/o E) 30.13CBDNet(w/o TV) 30.07CBDNet(w/o A) 30.38CBDNet 30.39CBDNet(σGT ) 31.69

Salt & Papper, d = 0.4

CBDNet(w/o E) 27.47CBDNet(w/o TV) 28.13CBDNet(w/o A) 28.23CBDNet 28.46

Salt & Papper, d = 0.8

CBDNet(w/o E) 25.98CBDNet(w/o TV) 26.55CBDNet(w/o A) 26.82CBDNet 27.12

Table 2: The quantitative results on noise estimation.Methods RMSE

Indcrf =5, pattern = ’gbrg’σs = 0.08, σc = 0.03

CBDNet(w/o TV) 0.0153

CBDNet 0.0133

Indcrf =5, pattern = ’gbrg’σs = 0.10, σc = 0.05

CBDNet(w/o TV) 0.0205

CBDNet 0.0169

(a) ground-truth (b) CBDNet (c) CBDNet(w/o TV)0

Figure 1: Visual results of estimated noise map

(a) Noisy image (b) CBDNet(w/o Per) (c) CBDNetFigure 2: Visual results of CBDNet with & without

perceptual loss.

References[1] Abdelrahman Abdelhamed, Stephen Lin, and Michael S.

Brown. A high-quality denoising dataset for smartphone cam-eras. In The IEEE Conference on Computer Vision and PatternRecognition (CVPR), June 2018. 1

[2] Neatlab ABSoft. Neat image. https://ni.neatvideo.com/home. 3, 4, 5, 6, 7

[3] Kostadin Dabov, Alessandro Foi, Vladimir Katkovnik, andKaren O. Egiazarian. Color image denoising via sparse 3dcollaborative filtering with grouping constraint in luminance-chrominance space. 2007 IEEE International Conference onImage Processing, 1:I – 313–I – 316, 2007. 3, 4, 5, 6, 7

[4] Marc Lebrun, Miguel Colom, and Jean-Michel Morel. Thenoise clinic: a blind image denoising algorithm. IPOL Jour-nal, 5:1–54, 2015. 3, 4, 5, 6, 7

[5] Tobias Plotz and Stefan Roth. Benchmarking denoising algo-rithms with real photographs. 2017 IEEE Conference on Com-puter Vision and Pattern Recognition (CVPR), pages 2750–2759, 2017. 1

[6] Jun Xu, Lei Zhang, and David Zhang. A trilateral weightedsparse coding scheme for real-world image denoising. In Eu-ropean Conference on Computer Vision, 2018. 3, 4, 5, 6, 7

[7] Jun Xu, Lei Zhang, David Zhang, and Xiangchu Feng. Multi-channel weighted nuclear norm minimization for real colorimage denoising. 2017 IEEE International Conference onComputer Vision (ICCV), pages 1105–1113, 2017. 3, 4, 5,6, 7

[8] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, andLei Zhang. Beyond a gaussian denoiser: Residual learning ofdeep cnn for image denoising. IEEE Transactions on ImageProcessing, 26:3142–3155, 2017. 3, 4, 5, 6, 7

(a) Noisy image (b) BM3D [3] (c) CDnCNN-B [8] (d) NC [4]

(e) NI [2] (f) MCWNNM [7] (g) TWSC [6] (h) CBDNetFigure 3: Denoising results of a DND image by different methods.

(e) NI [2] (f) MCWNNM [7] (g) TWSC [6] (h) CBDNetFigure 10: Denoising results of a SIDD image by different methods.

Toward Convolutional Blind Denoising of Real Photographs ......Toward Convolutional Blind Denoising...

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