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，用于鱼眼图像校正并同时标定相机内参及畸变参数。该方法包含两个部分：基于Pix2PixGAN与Wasserstein GAN（W-Pix2PixGAN）的快速图像校正模块，以及基于CNN架构的标定模块。快速校正网络在保持良好分辨率的同时实现鲁棒校正，适用于基于相机的监控设备中的恒定标定任务。为实现高质量标定，我们将快速校正模块输出的拉直图像作为类引导语义特征图，供标定模块学习拉直特征与畸变特征之间的几何关系。我们使用包含经过良好仿真参数标注的大规模合成数据集对该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，PSNR值达到22.343的显著水平。