DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time
DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time
复制标题
DR-GAN：实时使用条件 GAN 进行自动径向畸变校正
DOI：
10.1109/tcsvt.2019.2897984
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发表时间：
2020-03-01
影响因子：
8.4
通讯作者：
Gabbouj, Moncef
中科院分区：
文献类型：
--
作者：
Liao, Kang;Lin, Chunyu;Gabbouj, Moncef
Radial distortion, which severely hinders object detection and semantic recognition, frequently exists in images captured using a wide-angle lens. Correction of this distortion of images is crucial in many computer vision applications. In this paper, we present distortion rectification generative adversarial network (DR-GAN), a conditional generative adversarial network (GAN) for automatic radial DR. To the best of our knowledge, this is the first end-to-end trainable adversarial framework for radial distortion rectification. The DR-GAN trained using the proposed low-to-high perceptual loss learns the mapping relation between different structural images rather than estimating multifarious distortion parameters, while also realizing label-free training and one-stage rectification. As a benefit of one-stage rectification, the proposed method is extremely fast with the completion of rectification in real time. This is approximately 22 times faster than the state-of-the-art methods. The experimental results show that the DR-GAN achieves an excellent performance in both quantitative measure (PSNR and SSIM) and visual qualitative appearance.
