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：我们提出了一种端到端的深度学习方法，用于矫正鱼眼图像并同时标定相机内参和畸变参数。该方法由两个部分组成：基于Pix2Pix GAN与Wasserstein GAN（W-Pix2PixGAN）的快速图像矫正模块，以及采用CNN架构的标定模块。快速矫正网络能够在保持良好分辨率的前提下实现鲁棒矫正，适用于基于相机的监控设备中的恒定标定。为实现高质量标定，我们利用快速矫正模块的输出（已矫正特征图）作为类似引导的语义特征图，供标定模块学习矫正特征与畸变特征之间的几何关系。我们使用一个大型合成数据集进行训练和验证，该数据集的标签采用对透视图像数据集施加良好模拟参数的方式生成。我们的方案在高分辨率下实现了鲁棒性能，PSNR值显著达到22.343。