We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters. Our method consists of two parts: a Quick Image Rectification Module developed with a Pix2Pix GAN and Wasserstein GAN (W-Pix2PixGAN), and a Calibration Module with a CNN architecture. Our Quick Rectification Network performs robust rectification with good resolution, making it suitable for constant calibration in camera-based surveillance equipment. To achieve high-quality calibration, we use the straightened output from the Quick Rectification Module as a guidance-like semantic feature map for the Calibration Module to learn the geometric relationship between the straightened feature and the distorted feature. We train and validate our method with a large synthesized dataset labeled with well-simulated parameters applied to a perspective image dataset. Our solution has achieved robust performance in high-resolution with a significant PSNR value of 22.343.

翻译：我们提出一种端到端的深度学习方法，用于纠正鱼眼图像并同时标定相机内参和畸变参数。该方法包含两个部分：基于Pix2PixGAN和Wasserstein GAN的快速图像校正模块（W-Pix2PixGAN），以及采用CNN架构的标定模块。快速校正网络能以良好分辨率实现鲁棒校正，使其适用于基于摄像头的监控设备的持续标定。为获得高质量标定，我们将快速校正模块产生的直线化输出作为类似引导语义的特征图，供标定模块学习直线化特征与畸变特征之间的几何关系。我们使用包含良好仿真参数标注的大规模合成数据集对方法进行训练与验证。该方案在高分辨率下实现了鲁棒性能，峰值信噪比（PSNR）值达到22.343。