Modeling imaging sensor noise is a fundamental problem for image processing and computer vision applications. While most previous works adopt statistical noise models, real-world noise is far more complicated and beyond what these models can describe. To tackle this issue, we propose a data-driven approach, where a generative noise model is learned from real-world noise. The proposed noise model is camera-aware, that is, different noise characteristics of different camera sensors can be learned simultaneously, and a single learned noise model can generate different noise for different camera sensors. Experimental results show that our method quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods.
@article{arxiv.2008.09370,
title = {Learning Camera-Aware Noise Models},
author = {Ke-Chi Chang and Ren Wang and Hung-Jin Lin and Yu-Lun Liu and Chia-Ping Chen and Yu-Lin Chang and Hwann-Tzong Chen},
journal= {arXiv preprint arXiv:2008.09370},
year = {2020}
}