Automatic radial un-distortion using conditional generative adversarial network

被引：7
作者：
Park D.-H. [1 ]
Kakani V. [2 ]
Kim H.-I. [1 ,2 ]
机构：
[1] School of Future Vehicle Engineering, Inha University, Incheon
[2] School of Information and Communication Engineering, Inha University, Incheon
来源：
Journal of Institute of Control, Robotics and Systems | 2019年 / 25卷 / 11期
关键词：
Conditional Generative Adversarial Network; Deep learning; Object Detection; Radial Un-distortion;
D O I：
10.5302/J.ICROS.2019.19.0121
中图分类号：
学科分类号：
摘要：
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. © ICROS 2019.
引用
收藏
页码：1007 / 1013
页数：6
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