A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras
Journal:
arXiv
Published Date:
Apr 9, 2025
Abstract
Pan-Tilt-Zoom (PTZ) cameras with wide-angle lenses are widely used in
surveillance but often require image rectification due to their inherent
nonlinear distortions. Current deep learning approaches typically struggle to
maintain fine-grained geometric details, resulting in inaccurate rectification.
This paper presents a Forward Distortion and Backward Warping Network
(FDBW-Net), a novel framework for wide-angle image rectification. It begins by
using a forward distortion model to synthesize barrel-distorted images,
reducing pixel redundancy and preventing blur. The network employs a pyramid
context encoder with attention mechanisms to generate backward warping flows
containing geometric details. Then, a multi-scale decoder is used to restore
distorted features and output rectified images. FDBW-Net's performance is
validated on diverse datasets: public benchmarks, AirSim-rendered PTZ camera
imagery, and real-scene PTZ camera datasets. It demonstrates that FDBW-Net
achieves SOTA performance in distortion rectification, boosting the
adaptability of PTZ cameras for practical visual applications.
