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arXiv · 1909.03459

Blind Geometric Distortion Correction on Images Through Deep Learning

Xiaoyu Li·Bo Zhang·Pedro V. Sander·Jing Liao
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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.

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Xiaoyu Li, Bo Zhang, Pedro V. Sander, Jing Liao. 2019-09-08. Blind Geometric Distortion Correction on Images Through Deep Learning. https://arxiv.org/abs/1909.03459

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