Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation

Ronnachai Jaroensri

Abstract:

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.