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• W2979543786 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." @default.
• W2979543786 created "2019-10-18" @default.
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• W2979543786 date "2019-06-01" @default.
• W2979543786 modified "2023-10-01" @default.
• W2979543786 title "Blind Geometric Distortion Correction on Images Through Deep Learning" @default.
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• W2979543786 doi "https://doi.org/10.1109/cvpr.2019.00499" @default.
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