  Noise modeling and reduction are fundamental tasks in low-level computer
vision. They are particularly important for smartphone cameras relying on small
sensors that exhibit visually noticeable noise. There has recently been renewed
interest in using data-driven approaches to improve camera noise models via
neural networks. These data-driven approaches target noise present in the
raw-sensor image before it has been processed by the camera's image signal
processor (ISP). Modeling noise in the RAW-rgb domain is useful for improving
and testing the in-camera denoising algorithm; however, there are situations
where the camera's ISP does not apply denoising or additional denoising is
desired when the RAW-rgb domain image is no longer available. In such cases,
the sensor noise propagates through the ISP to the final rendered image encoded
in standard RGB (sRGB). The nonlinear steps on the ISP culminate in a
significantly more complex noise distribution in the sRGB domain and existing
raw-domain noise models are unable to capture the sRGB noise distribution. We
propose a new sRGB-domain noise model based on normalizing flows that is
capable of learning the complex noise distribution found in sRGB images under
various ISO levels. Our normalizing flows-based approach outperforms other
models by a large margin in noise modeling and synthesis tasks. We also show
that image denoisers trained on noisy images synthesized with our noise model
outperforms those trained with noise from baselines models.
