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

arXiv: https://arxiv.org/abs/2305.05222 · HTML: https://mlsanity.com/paper/2305.05222/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification](https://mlsanity.com/paper/1804.04784/) (2018)
- [Learning to Calibrate Straight Lines for Fisheye Image Rectification](https://mlsanity.com/paper/1904.09856/) (2019)
- [Multi-Stage Pathological Image Classification using Semantic Segmentation](https://mlsanity.com/paper/1910.04473/) (2019)
- [Complex Network Classification with Convolutional Neural Network](https://mlsanity.com/paper/1802.00539/) (2018)
- [Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema](https://mlsanity.com/paper/2009.05403/) (2020)
- [Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection](https://mlsanity.com/paper/2305.04379/) (2023)

## Built on by (most cited first, 2 of 2)

- [G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer](https://mlsanity.com/paper/2306.14314/) (2023)
- [FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters](https://mlsanity.com/paper/2610.02799/) (2026)

## Abstract (arXiv)

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.