  We investigate the task of learning blind image denoising networks from an
unpaired set of clean and noisy images. Such problem setting generally is
practical and valuable considering that it is feasible to collect unpaired
noisy and clean images in most real-world applications. And we further assume
that the noise can be signal dependent but is spatially uncorrelated. In order
to facilitate unpaired learning of denoising network, this paper presents a
two-stage scheme by incorporating self-supervised learning and knowledge
distillation. For self-supervised learning, we suggest a dilated blind-spot
network (D-BSN) to learn denoising solely from real noisy images. Due to the
spatial independence of noise, we adopt a network by stacking 1x1 convolution
layers to estimate the noise level map for each image. Both the D-BSN and
image-specific noise model (CNN\_est) can be jointly trained via maximizing the
constrained log-likelihood. Given the output of D-BSN and estimated noise level
map, improved denoising performance can be further obtained based on the Bayes'
rule. As for knowledge distillation, we first apply the learned noise models to
clean images to synthesize a paired set of training images, and use the real
noisy images and the corresponding denoising results in the first stage to form
another paired set. Then, the ultimate denoising model can be distilled by
training an existing denoising network using these two paired sets. Experiments
show that our unpaired learning method performs favorably on both synthetic
noisy images and real-world noisy photographs in terms of quantitative and
qualitative evaluation.
