Non-parametric Sensor Noise Modeling and Synthesis

Ali Mosleh, Luxi Zhao, Atin Vikram Singh, Jaeduk Han, Abhijith Punnappurath, Marcus A Brubaker, Jihwan Choe, Michael S. Brown

Abstract: We introduce a novel non-parametric sensor noise model that directly constructs probability mass functions per intensity level from captured images. We show that our noise model provides a more accurate fit to real sensor noise than existing models. We detail the capture procedure for deriving our non-parametric noise model and introduce an interpolation method that reduces the number of ISOs levels that need to be captured. In addition, we propose a method to synthesize noise on existing noisy images when noise-free images are not available. Our noise model is straightforward to calibrate and provides notable improvements over competing noise models on downstream tasks.