A Comprehensive Overview of Fish-Eye Camera Distortion Correction Methods
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Panorama Generation From NFoV Image Done Right
LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion
2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion Correction
SelfDRSC++: Self-Supervised Learning for Dual Reversed Rolling Shutter Correction
Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
PyHySCO: GPU-Enabled Susceptibility Artifact Distortion Correction in Seconds
토론
이 논문과 연결된 포럼 글이 아직 없습니다.