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.

我们提出了第一个通用框架，用于自动纠正单个输入图像中不同类型的几何畸变。我们的方法采用卷积神经网络（CNN），通过使用大型合成畸变数据集进行训练，以预测扭曲图像和校正图像之间的位移场。模型拟合方法使用 CNN 输出估计畸变参数，实现更准确的预测，并使用高效，高质量的重采样方法生成最终的校正图像。实验结果表明，我们的算法优于传统的校正方法，可实现有趣的应用，如畸变转移，畸变夸张和共同发生的畸变校正。