  The effectiveness of existing denoising algorithms typically relies on
accurate pre-defined noise statistics or plenty of paired data, which limits
their practicality. In this work, we focus on denoising in the more common case
where noise statistics and paired data are unavailable. Considering that
denoising CNNs require supervision, we develop a new \textbf{adaptive noise
imitation (ADANI)} algorithm that can synthesize noisy data from naturally
noisy images. To produce realistic noise, a noise generator takes unpaired
noisy/clean images as input, where the noisy image is a guide for noise
generation. By imposing explicit constraints on the type, level and gradient of
noise, the output noise of ADANI will be similar to the guided noise, while
keeping the original clean background of the image. Coupling the noisy data
output from ADANI with the corresponding ground-truth, a denoising CNN is then
trained in a fully-supervised manner. Experiments show that the noisy data
produced by ADANI are visually and statistically similar to real ones so that
the denoising CNN in our method is competitive to other networks trained with
external paired data.
