AIR-CNN: a lightweight automatic image rectification CNN used for barrel distortion

Abstract

Abstract Barrel distortions often exist in images captured by wide-angle lenses, and the presence of barrel distortions reduces the label-making accuracy of algorithms and the precision rate of final target detection and semantic recognition. To reduce the interference of barrel distortions on imaging, we have proposed a lightweight image rectification network automatic image rectification CNN (AIR-CNN) for barrel distortion. The network is based on a parameter sharing (PS) convolutional neural network structure, which is trained on the distorted image dataset to predict the pixel displacement field between the distorted image and the rectified image, and finally restores the rectified image based on the predicted pixel displacement field. The experimental results show that the AIR-CNN can greatly reduce the number of network parameters through the PS mechanism and implicitly learns the texture features by asymmetric convolution kernels to obtain higher rectification accuracy at a lower computational cost, and automatically obtain the distortion parameters of the camera without special calibration methods. The AIR-CNN outperforms previous image rectification methods in both intuitive and quantitative comparisons (EPE, PSNR, NRMSE, SSIM).