  Modeling and synthesizing image noise is an important aspect in many computer
vision applications. The long-standing additive white Gaussian and
heteroscedastic (signal-dependent) noise models widely used in the literature
provide only a coarse approximation of real sensor noise. This paper introduces
Noise Flow, a powerful and accurate noise model based on recent normalizing
flow architectures. Noise Flow combines well-established basic parametric noise
models (e.g., signal-dependent noise) with the flexibility and expressiveness
of normalizing flow networks. The result is a single, comprehensive, compact
noise model containing fewer than 2500 parameters yet able to represent
multiple cameras and gain factors. Noise Flow dramatically outperforms existing
noise models, with 0.42 nats/pixel improvement over the camera-calibrated noise
level functions, which translates to 52% improvement in the likelihood of
sampled noise. Noise Flow represents the first serious attempt to go beyond
simple parametric models to one that leverages the power of deep learning and
data-driven noise distributions.
