  Lacking rich and realistic data, learned single image denoising algorithms
generalize poorly to real raw images that do not resemble the data used for
training. Although the problem can be alleviated by the heteroscedastic
Gaussian model for noise synthesis, the noise sources caused by digital camera
electronics are still largely overlooked, despite their significant effect on
raw measurement, especially under extremely low-light condition. To address
this issue, we present a highly accurate noise formation model based on the
characteristics of CMOS photosensors, thereby enabling us to synthesize
realistic samples that better match the physics of image formation process.
Given the proposed noise model, we additionally propose a method to calibrate
the noise parameters for available modern digital cameras, which is simple and
reproducible for any new device. We systematically study the generalizability
of a neural network trained with existing schemes, by introducing a new
low-light denoising dataset that covers many modern digital cameras from
diverse brands. Extensive empirical results collectively show that by utilizing
our proposed noise formation model, a network can reach the capability as if it
had been trained with rich real data, which demonstrates the effectiveness of
our noise formation model.
