  The success of deep denoisers on real-world color photographs usually relies
on the modeling of sensor noise and in-camera signal processing (ISP) pipeline.
Performance drop will inevitably happen when the sensor and ISP pipeline of
test images are different from those for training the deep denoisers (i.e.,
noise discrepancy). In this paper, we present an unpaired learning scheme to
adapt a color image denoiser for handling test images with noise discrepancy.
We consider a practical training setting, i.e., a pre-trained denoiser, a set
of test noisy images, and an unpaired set of clean images. To begin with, the
pre-trained denoiser is used to generate the pseudo clean images for the test
images. Pseudo-ISP is then suggested to jointly learn the pseudo ISP pipeline
and signal-dependent rawRGB noise model using the pairs of test and pseudo
clean images. We further apply the learned pseudo ISP and rawRGB noise model to
clean color images to synthesize realistic noisy images for denoiser adaption.
Pseudo-ISP is effective in synthesizing realistic noisy sRGB images, and
improved denoising performance can be achieved by alternating between
Pseudo-ISP training and denoiser adaption. Experiments show that our Pseudo-ISP
not only can boost simple Gaussian blurring-based denoiser to achieve
competitive performance against CBDNet, but also is effective in improving
state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.
