A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras

• URL: http://arxiv.org/abs/2504.06965v1
• Date: Wed, 09 Apr 2025 15:19:38 GMT
• Title: A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras
• Authors: Teng Xiao, Qi Hu, Qingsong Yan, Wei Liu, Zhiwei Ye, Fei Deng,
• Abstract summary: This paper presents a Forward Distortion and Backward Warping Network (FDBWNet), a novel framework for wide-grained image rectification.<n>It begins by using a forward distortion model synthesize barreldistorted images, reducing pixel redundancy and preventing blur.<n>The network employs a pyramid context with attention mechanisms generate backward warping flows containing geometric details.
• Score: 21.404790627439954
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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