Modeling and synthesizing real sRGB noise is crucial for various low-level vision tasks. 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: https://github.com/YoungJooHan/NM-FlowGAN

采用数据驱动的生成模型，NM-FlowGAN 通过同时使用基于正则化流的像素级噪声建模网络和基于生成对抗网络的空间相关建模网络，优于其他基准模型，实现了对sRGB噪声的更准确建模和合成。

NM-FlowGAN: 基于正则化流和生成对抗网络的混合方法建模sRGB噪声