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Modeling and synthesizing real sRGB noise is crucial for various low-levelvision tasks. The distribution of real sRGB noise is highly complex andaffected by a multitude of factors, making its accurate modeling extremelychallenging. Therefore, recent studies have proposed methods that employdata-driven generative models, such as generative adversarial networks (GAN)and Normalizing Flows. These studies achieve more accurate modeling of sRGBnoise compared to traditional noise modeling methods. However, there areperformance limitations due to the inherent characteristics of each generativemodel. To address this issue, we propose NM-FlowGAN, a hybrid approach thatexploits the strengths of both GAN and Normalizing Flows. We simultaneouslyemploy a pixel-wise noise modeling network based on Normalizing Flows, andspatial correlation modeling networks based on GAN. In our experiments, ourNM-FlowGAN outperforms other baselines on the sRGB noise synthesis task.Moreover, the denoising neural network, trained with synthesized image pairsfrom our model, also shows superior performance compared to other baselines.Our code is available at: https://github.com/YoungJooHan/NM-FlowGAN

NM-FlowGAN: Modeling sRGB Noise with a Hybrid Approach based on Normalizing Flows and Generative Adversarial Networks