  Deep image denoising networks have achieved impressive success with the help
of a considerably large number of synthetic train datasets. However, real-world
denoising is a still challenging problem due to the dissimilarity between
distributions of real and synthetic noisy datasets. Although several real-world
noisy datasets have been presented, the number of train datasets (i.e., pairs
of clean and real noisy images) is limited, and acquiring more real noise
datasets is laborious and expensive. To mitigate this problem, numerous
attempts to simulate real noise models using generative models have been
studied. Nevertheless, previous works had to train multiple networks to handle
multiple different noise distributions. By contrast, we propose a new
generative model that can synthesize noisy images with multiple different noise
distributions. Specifically, we adopt recent contrastive learning to learn
distinguishable latent features of the noise. Moreover, our model can generate
new noisy images by transferring the noise characteristics solely from a single
reference noisy image. We demonstrate the accuracy and the effectiveness of our
noise model for both known and unknown noise removal.
