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.
@article{arxiv.1909.03459,
title = {Blind Geometric Distortion Correction on Images Through Deep Learning},
author = {Xiaoyu Li and Bo Zhang and Pedro V. Sander and Jing Liao},
journal= {arXiv preprint arXiv:1909.03459},
year = {2019}
}