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While deep convolutional neural networks (CNNs) have achieved impressivesuccess in image denoising with additive white Gaussian noise (AWGN), theirperformance remains limited on real-world noisy photographs. The main reason isthat their learned models are easy to overfit on the simplified AWGN modelwhich deviates severely from the complicated real-world noise model. In orderto improve the generalization ability of deep CNN denoisers, we suggesttraining a convolutional blind denoising network (CBDNet) with more realisticnoise model and real-world noisy-clean image pairs. On the one hand, bothsignal-dependent noise and in-camera signal processing pipeline is consideredto synthesize realistic noisy images. On the other hand, real-world noisyphotographs and their nearly noise-free counterparts are also included to trainour CBDNet. To further provide an interactive strategy to rectify denoisingresult conveniently, a noise estimation subnetwork with asymmetric learning tosuppress under-estimation of noise level is embedded into CBDNet. Extensiveexperimental results on three datasets of real-world noisy photographs clearlydemonstrate the superior performance of CBDNet over state-of-the-arts in termsof quantitative metrics and visual quality. The code has been made available athttps://github.com/GuoShi28/CBDNet.

Toward Convolutional Blind Denoising of Real Photographs