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
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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."
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