Learning Camera-Aware Noise Models
Abstract
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. The source code and more results are available at https://arcchang1236.github.io/CA-NoiseGAN/
Cite
Text
Chang et al. "Learning Camera-Aware Noise Models." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58586-0_21Markdown
[Chang et al. "Learning Camera-Aware Noise Models." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/chang2020eccv-learning/) doi:10.1007/978-3-030-58586-0_21BibTeX
@inproceedings{chang2020eccv-learning,
title = {{Learning Camera-Aware Noise Models}},
author = {Chang, Ke-Chi and Wang, Ren and Lin, Hung-Jin and Liu, Yu-Lun and Chen, Chia-Ping and Chang, Yu-Lin and Chen, Hwann-Tzong},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58586-0_21},
url = {https://mlanthology.org/eccv/2020/chang2020eccv-learning/}
}