DR-GAN: Automatic Radial Distortion Rectification Using Conditional GAN in Real-Time
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||IEEE Transactions on Circuits and Systems for Video Technology|
|Publication status||E-pub ahead of print - 7 Feb 2019|
|Publication type||A1 Journal article-refereed|
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 study, we present DR-GAN, a conditional generative adversarial network (GAN) for automatic radial distortion rectification (DR). To the best of our knowledge, this is the first end-to-end trainable adversarial framework for radial distortion rectification. 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 × faster than the state-of-the-art methods. The experimental results show that DR-GAN achieves excellent performance in both quantitative measure (PSNR and SSIM) and visual qualitative appearance.