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
this https URL for comparisons.

该研究提出了一种生成高度仿真的实际噪声图像的方法，为深度学习去噪提供了更加真实的数据集，克服了过去需要大量真实噪声 - 干净图像对来进行监督的限制。该方法使用噪声模型进行噪声分布对齐和图像对齐，并通过 Pixel-level 噪声感知生成对抗网络（PNGAN）进行像素级别的噪声域对齐，实现了高质量的去噪效果。