  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.
