  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.
