Blind Geometric Distortion Correction on Images Through Deep Learning
Abstract
We propose the first general framework to automatically correct different types of geometric distortion in a single input image. Our proposed method employs convolutional neural networks (CNNs) trained by using a large synthetic distortion dataset to predict the displacement field between distorted images and corrected images. A model fitting method uses the CNN output to estimate the distortion parameters, achieving a more accurate prediction. The final corrected image is generated based on the predicted flow using an efficient, high-quality resampling method. Experimental results demonstrate that our algorithm outperforms traditional correction methods, and allows for interesting applications such as distortion transfer, distortion exaggeration, and co-occurring distortion correction.
Cite
Text
Li et al. "Blind Geometric Distortion Correction on Images Through Deep Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00499Markdown
[Li et al. "Blind Geometric Distortion Correction on Images Through Deep Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/li2019cvpr-blind/) doi:10.1109/CVPR.2019.00499BibTeX
@inproceedings{li2019cvpr-blind,
title = {{Blind Geometric Distortion Correction on Images Through Deep Learning}},
author = {Li, Xiaoyu and Zhang, Bo and Sander, Pedro V. and Liao, Jing},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2019},
doi = {10.1109/CVPR.2019.00499},
url = {https://mlanthology.org/cvpr/2019/li2019cvpr-blind/}
}