Automatic radial un-distortion using conditional generative adversarial network

This article describes a method for radial un-distortion of image using a conditional generative adversarial network. The proposed network consists of a generator which has a similar shape of U-Net and a shallow discriminator. The proposed model is trained by using perceptual loss, content loss and adversarial loss over the PASCAL VOC datasets where each sample image is distorted by one-parameter radial distortion model and inserted as a condition. The experimental results are compared with traditional radial un-distortion models such as Bukhari’s and Rong’s methods, and demonstrate not only 12-times faster distortion correction speeds but also a significant improvement in PSNR and SSIM. Additionally, the corrected images show an improved performance in object detection.