Toward Convolutional Blind Denoising of Real Photographs

CVPR 2019  ·  Shi Guo, Zifei Yan, Kai Zhang, WangMeng Zuo, Lei Zhang ·

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model... In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative metrics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet. read more

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Color Image Denoising Darmstadt Noise Dataset CBDNet (Blind) PSNR (sRGB) 38.06 # 4
SSIM (sRGB) 0.9421 # 4
Denoising Darmstadt Noise Dataset CBDNet(Syn) PSNR 37.57 # 3
Image Denoising DND CBDNet PSNR (sRGB) 38.06 # 12
SSIM (sRGB) 0.942 # 12
Image Denoising SIDD CBDNet PSNR (sRGB) 30.78 # 12
SSIM (sRGB) 0.801 # 12

Methods


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