为了增强畸变校正方法的实时性和适用性,提出了一种基于卷积神经网络(CNN)的图像畸变校正方法。首先,使用具有自校准功能的运动结构重建真实相机拍摄的图像序列,以估计相机参数;然后,根据拟合出的第一、第二阶径向畸变参数之间的函数关系,生成常见径向畸变范围内的图像,解决带有第一、第二阶径向畸变注释的畸变图像较少的问题;最后,利用CNN强大的学习能力学习径向畸变的特征,以估计径向的变形情况,并将输入图像映射为畸变系数,实现图像的畸变校正。实验结果表明,相比传统相机标定法,本方法的校正误差约为1 pixel。

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.