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Modeling and synthesizing real sRGB noise is crucial for various low-levelvision tasks, such as building datasets for training image denoising systems.The distribution of real sRGB noise is highly complex and affected by amultitude of factors, making its accurate modeling extremely challenging.Therefore, recent studies have proposed methods that employ data-drivengenerative models, such as Generative Adversarial Networks (GAN) andNormalizing Flows. These studies achieve more accurate modeling of sRGB noisecompared to traditional noise modeling methods. However, there are performancelimitations due to the inherent characteristics of each generative model. Toaddress this issue, we propose NM-FlowGAN, a hybrid approach that exploits thestrengths of both GAN and Normalizing Flows. We combine pixel-wise noisemodeling networks based on Normalizing Flows and spatial correlation modelingnetworks based on GAN. Specifically, the pixel-wise noise modeling networkleverages the high training stability of Normalizing Flows to capture noisecharacteristics that are affected by a multitude of factors, and the spatialcorrelation networks efficiently model pixel-to-pixel relationships. Inparticular, unlike recent methods that rely on paired noisy images, our methodsynthesizes noise using clean images and factors that affect noisecharacteristics, such as easily obtainable parameters like camera type and ISOsettings, making it applicable to various fields where obtaining noisy-cleanimage pairs is not feasible. In our experiments, our NM-FlowGAN outperformsother baselines in the sRGB noise synthesis task. Moreover, the denoisingneural network trained with synthesized image pairs from our model showssuperior performance compared to other baselines. Our code is available at:\url{https://github.com/YoungJooHan/NM-FlowGAN}.

NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN