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• W2971977001 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.
• W2971977001 created "2019-09-12" @default.
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• W2971977001 date "2019-09-08" @default.
• W2971977001 modified "2023-09-23" @default.
• W2971977001 title "Blind Geometric Distortion Correction on Images Through Deep Learning" @default.
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• W2971977001 doi "https://doi.org/10.48550/arxiv.1909.03459" @default.
• W2971977001 hasPublicationYear "2019" @default.
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