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.

介绍了基于normalizing flow的强大而精确的噪声模型Noise Flow，结合了基本参数噪声模型和神经网络的灵活性和表现力。与已有的噪声模型相比，Noise Flow具有显著的改进，是第一次试图超越简单的参数模型，利用深度学习和数据驱动的噪声分布来建模和综合图像噪声。

噪声流：带条件的标准化流噪声建模