Image reconstruction techniques such as denoising often need to be applied to
the RGB output of cameras and cellphones. Unfortunately, the commonly used
additive white noise (AWGN) models do not accurately reproduce the noise and
the degradation encountered on these inputs. This is particularly important for
learning-based techniques, because the mismatch between training and real world
data will hurt their generalization. This paper aims to accurately simulate the
degradation and noise transformation performed by camera pipelines. This allows
us to generate realistic degradation in RGB images that can be used to train
machine learning models. We use our simulation to study the importance of noise
modeling for learning-based denoising. Our study shows that a realistic noise
model is required for learning to denoise real JPEG images. A neural network
trained on realistic noise outperforms the one trained with AWGN by 3 dB. An
ablation study of our pipeline shows that simulating denoising and demosaicking
is important to this improvement and that realistic demosaicking algorithms,
which have been rarely considered, is needed. We believe this simulation will
also be useful for other image reconstruction tasks, and we will distribute our
code publicly.

本文研究基于真实噪声模型的图像降噪方法，在相机管道中模拟真实的图像噪声和降噪，发现噪声模型对学习模型效果的提升有重要作用，通过模拟降噪和图片拼合进而提高噪声模型的真实度和效果。