FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration

• URL: http://arxiv.org/abs/2305.05222v3
• Date: Wed, 12 Jul 2023 18:55:53 GMT
• Title: FishRecGAN: An End to End GAN Based Network for Fisheye Rectification and Calibration
• Authors: Xin Shen, Kyungdon Joo, Jean Oh
• Abstract summary: We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera and distortion parameters. Our solution has achieved robust performance in high-resolution with a significant PSNR value of 22.343.
• Score: 21.816020192280977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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