A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methods
It addresses image quality issues for users in fields relying on fisheye cameras, but it is incremental as it synthesizes existing methods without introducing new techniques.
This review tackles the problem of distortion in fisheye camera images by providing a comprehensive overview of correction methods, including polynomial models, panorama mapping, and deep learning, to enhance image quality for various applications.
The fisheye camera, with its unique wide field of view and other characteristics, has found extensive applications in various fields. However, the fisheye camera suffers from significant distortion compared to pinhole cameras, resulting in distorted images of captured objects. Fish-eye camera distortion is a common issue in digital image processing, requiring effective correction techniques to enhance image quality. This review provides a comprehensive overview of various methods used for fish-eye camera distortion correction. The article explores the polynomial distortion model, which utilizes polynomial functions to model and correct radial distortions. Additionally, alternative approaches such as panorama mapping, grid mapping, direct methods, and deep learning-based methods are discussed. The review highlights the advantages, limitations, and recent advancements of each method, enabling readers to make informed decisions based on their specific needs.