FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration
This addresses fisheye distortion correction for camera-based surveillance equipment, representing an incremental improvement over existing methods.
The paper tackles the problem of rectifying fisheye images and calibrating camera parameters using an end-to-end deep learning approach, achieving robust performance with a PSNR of 22.343.
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.