  Modeling and synthesizing low-light raw noise is a fundamental problem for
computational photography and image processing applications. Although most
recent works have adopted physics-based models to synthesize noise, the
signal-independent noise in low-light conditions is far more complicated and
varies dramatically across camera sensors, which is beyond the description of
these models. To address this issue, we introduce a new perspective to
synthesize the signal-independent noise by a generative model. Specifically, we
synthesize the signal-dependent and signal-independent noise in a physics- and
learning-based manner, respectively. In this way, our method can be considered
as a general model, that is, it can simultaneously learn different noise
characteristics for different ISO levels and generalize to various sensors.
Subsequently, we present an effective multi-scale discriminator termed Fourier
transformer discriminator (FTD) to distinguish the noise distribution
accurately. Additionally, we collect a new low-light raw denoising (LRD)
dataset for training and benchmarking. Qualitative validation shows that the
noise generated by our proposed noise model can be highly similar to the real
noise in terms of distribution. Furthermore, extensive denoising experiments
demonstrate that our method performs favorably against state-of-the-art methods
on different sensors.

Computer Vision and Pattern Recognition;Multimedia;Image and Video Processing