  Denoising extreme low light images is a challenging task due to the high
noise level. When the illumination is low, digital cameras increase the ISO
(electronic gain) to amplify the brightness of captured data. However, this in
turn amplifies the noise, arising from read, shot, and defective pixel sources.
In the raw domain, read and shot noise are effectively modelled using Gaussian
and Poisson distributions respectively, whereas defective pixels can be modeled
with impulsive noise. In extreme low light imaging, noise removal becomes a
critical challenge to produce a high quality, detailed image with low noise. In
this paper, we propose a multi-task deep neural network called Noise
Decomposition (NODE) that explicitly and separately estimates defective pixel
noise, in conjunction with Gaussian and Poisson noise, to denoise an extreme
low light image. Our network is purposely designed to work with raw data, for
which the noise is more easily modeled before going through non-linear
transformations in the image signal processing (ISP) pipeline. Quantitative and
qualitative evaluation show the proposed method to be more effective at
denoising real raw images than state-of-the-art techniques.
