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

arXiv: https://arxiv.org/abs/2504.06965 · HTML: https://mlsanity.com/paper/2504.06965/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Builds on (most cited first, 8 of 8)

- [OpenScene: 3D Scene Understanding with Open Vocabularies](https://mlsanity.com/paper/2211.15654/) (2022)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [GeoCalib: Learning Single-image Calibration with Geometric Optimization](https://mlsanity.com/paper/2409.06704/) (2024)
- [Blind Geometric Distortion Correction on Images Through Deep Learning](https://mlsanity.com/paper/1909.03459/) (2019)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [A Deep Ordinal Distortion Estimation Approach for Distortion Rectification](https://mlsanity.com/paper/2007.10689/) (2020)
- [Lens Distortion Rectification using Triangulation based Interpolation](https://mlsanity.com/paper/1611.09559/) (2016)
- [QueryCDR: Query-Based Controllable Distortion Rectification Network for Fisheye Images](https://mlsanity.com/paper/2412.13496/) (2024)

## Abstract (arXiv)

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.