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We propose the first general framework to automatically correct differenttypes of geometric distortion in a single input image. Our proposed methodemploys convolutional neural networks (CNNs) trained by using a large syntheticdistortion dataset to predict the displacement field between distorted imagesand corrected images. A model fitting method uses the CNN output to estimatethe distortion parameters, achieving a more accurate prediction. The finalcorrected image is generated based on the predicted flow using an efficient,high-quality resampling method. Experimental results demonstrate that ouralgorithm outperforms traditional correction methods, and allows forinteresting applications such as distortion transfer, distortion exaggeration,and co-occurring distortion correction.

Blind Geometric Distortion Correction on Images Through Deep Learning