  Modeling and synthesizing real sRGB noise is crucial for various low-level
vision tasks, such as building datasets for training image denoising systems.
The distribution of real sRGB noise is highly complex and affected by a
multitude of factors, making its accurate modeling extremely challenging.
Therefore, recent studies have proposed methods that employ data-driven
generative models, such as generative adversarial networks (GAN) and
Normalizing Flows. These studies achieve more accurate modeling of sRGB noise
compared to traditional noise modeling methods. However, there are performance
limitations due to the inherent characteristics of each generative model. To
address this issue, we propose NM-FlowGAN, a hybrid approach that exploits the
strengths of both GAN and Normalizing Flows. We simultaneously employ a
pixel-wise noise modeling network based on Normalizing Flows, and spatial
correlation modeling networks based on GAN. In our experiments, our NM-FlowGAN
outperforms other baselines on the sRGB noise synthesis task. Moreover, the
denoising neural network, trained with synthesized image pairs from our model,
also shows superior performance compared to other baselines. Our code is
available at: \url{https://github.com/YoungJooHan/NM-FlowGAN}.
