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Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
[article]
2019
arXiv
pre-print
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
arXiv:1904.08825v1
fatcat:i4nqz55oyfgyzlgohlpthdsmja