  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.
