  Existing deep learning real denoising methods require a large amount of
noisy-clean image pairs for supervision. Nonetheless, capturing a real
noisy-clean dataset is an unacceptable expensive and cumbersome procedure. To
alleviate this problem, this work investigates how to generate realistic noisy
images. Firstly, we formulate a simple yet reasonable noise model that treats
each real noisy pixel as a random variable. This model splits the noisy image
generation problem into two sub-problems: image domain alignment and noise
domain alignment. Subsequently, we propose a novel framework, namely
Pixel-level Noise-aware Generative Adversarial Network (PNGAN). PNGAN employs a
pre-trained real denoiser to map the fake and real noisy images into a nearly
noise-free solution space to perform image domain alignment. Simultaneously,
PNGAN establishes a pixel-level adversarial training to conduct noise domain
alignment. Additionally, for better noise fitting, we present an efficient
architecture Simple Multi-scale Network (SMNet) as the generator. Qualitative
validation shows that noise generated by PNGAN is highly similar to real noise
in terms of intensity and distribution. Quantitative experiments demonstrate
that a series of denoisers trained with the generated noisy images achieve
state-of-the-art (SOTA) results on four real denoising benchmarks. Part of
codes, pre-trained models, and results are available at
https://github.com/caiyuanhao1998/PNGAN for comparisons.
