Image geometric distortion correction is a key pre-processing step for many computer vision applications. Current geometric distortion correction methods based on deep learning mainly solve the problems of single distortion correction of images. For this reason, this paper proposes a hybrid distortion correction method for images with improved U-Net networks. Firstly, a method is proposed to construct hybrid distortion image datasets, which solves the problems of sparse training dataset and single distortion type. Secondly, the U-Net with spatial attention for image feature extraction and reconstruction of the distortion coordinate map is used to turn the image correction problem into a prediction problem of the pixel-by-pixel coordinate displacement change of the distorted image, and a loss function combining coordinate difference loss and image resampling loss is designed to effectively improve the correction accuracy. Finally, the performance of each module of the method in this paper is verified by ablation experiments. Compared with the latest deep learning-based distortion correction methods, the experimental results show that the method in this paper has better performance in terms of quantitative indexes and subjective evaluation, and reaches 0.251 9 of the mean absolute error for coordinate correction of distorted images. Calibration experiments on the optical images acquired by GoPro cameras have further verified the effectiveness of the proposed method in practice.