提出新方法提升全景云台相机图像校正精度。
A Deep Single Image Rectification Approach for Pan-Tilt-Zoom Cameras
- 先生成畸变图像，再逆向校正，保留几何细节。
- 在多个数据集上达到当前最佳效果，提升实用适应性。
- 适合需要高精度图像校正的安防与视觉系统应用。
广角镜头的全景云台（PTZ）相机广泛用于监控，但其固有的非线性畸变常需图像校正。现有深度学习方法难以保持精细几何结构，导致校正不准。本文提出前向畸变与后向变形网络（FDBW-Net），通过前向畸变模型合成桶形畸变图像，减少像素冗余并避免模糊；采用带注意力机制的金字塔上下文编码器生成包含几何细节的反向变形流；再通过多尺度解码器恢复特征并输出校正图像。FDBW-Net在公开基准、AirSim渲染的PTZ图像及真实场景数据集上验证，性能优于现有方法，显著提升PTZ相机在实际视觉任务中的适用性。
原文摘要 · Abstract (English)
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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