# Single Image Automatic Radial Distortion Compensation Using Deep Convolutional Network

arXiv: https://arxiv.org/abs/2112.08198 · HTML: https://mlsanity.com/paper/2112.08198/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [A Deep Ordinal Distortion Estimation Approach for Distortion Rectification](https://mlsanity.com/paper/2007.10689/) (2020)
- [Fisheye Distortion Rectification from Deep Straight Lines](https://mlsanity.com/paper/2003.11386/) (2020)

## Abstract (arXiv)

In many computer vision domains, the input images must conform with the pinhole camera model, where straight lines in the real world are projected as straight lines in the image. Performing computer vision tasks on live sports broadcast footage imposes challenging requirements where the algorithms cannot rely on a specific calibration pattern must be able to cope with unknown and uncalibrated cameras, radial distortion originating from complex television lenses, few visual clues to compensate distortion by, and the necessity for real-time performance. We present a novel method for single-image automatic lens distortion compensation based on deep convolutional neural networks, capable of real-time performance and accuracy using two highest-order coefficients of the polynomial distortion model operating in the application domain of sports broadcast. Keywords: Deep Convolutional Neural Network, Radial Distortion, Single Image Rectification