For the convenience and applicability of distortion correction methods, a distortion correction method based on convolutional neural networks is presented in this paper. First, the self-calibration functional motion structure is used to reconstruct the image sequence taken by the real camera to estimate the camera parameters. Second, according to the functional relationship between the first and second-order radial distortion parameters, the images within the common radial distortion range are generated to solve the problem of less distorted images with the first and second-order radial distortion annotation. Finally, by using the powerful learning ability of CNN, the radial distortion features are learned to estimate the radial deformation, and the input image is mapped to the distortion coefficient to realize the image distortion correction. Experimental results show that the calibration error of this method is about 1 pixel compared with the traditional camera calibration method.