Fisheye Image

Fisheye images, characterized by their wide field of view and significant radial distortion, present unique challenges for computer vision tasks. Current research focuses on developing robust rectification methods, often employing convolutional neural networks (CNNs) and transformers, to correct this distortion and adapt existing algorithms designed for perspective images. These efforts are driven by the increasing use of fisheye cameras in applications like autonomous driving and surveillance, where their wide FOV is crucial but distortion hinders accurate perception and analysis. Furthermore, research is addressing the impact of video compression and privacy concerns on fisheye imagery, highlighting the need for efficient and privacy-preserving processing techniques.