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Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images

  • Jiangpeng Rong
  • Shiyao Huang
  • Zeyu Shang
  • Xianghua YingEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

Radial lens distortion often exists in images taken by common cameras, which violates the assumption of pinhole camera model. Estimating the radial lens distortion of an image is an important preprocessing step for many vision applications. This paper intends to employ CNNs (Convolutional Neural Networks), to achieve radial distortion correction. However, the main issue hinder its progress is the scarcity of training data with radial distortion annotations. Inspired by the growing availability of image dataset with non-radial distortion, we propose a framework to address the issue by synthesizing images with radial distortion for CNNs. We believe that a large number of images of high variation of radial distortion is generated, which can be well exploited by deep CNN with a high learning capacity. We present quantitative results that demonstrate the ability of our technique to estimate the radial distortion with comparisons against several baseline methods, including an automatic method based on Hough transforms of distorted line images.

Keywords

Training Image Convolutional Neural Network Distorted Image Transfer Learning Target Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This work was supported in part by State Key Development Program Grand No. 2016YFB1001001, NNSFC Grant No. 61322309, and NNSFC Grant No. 61273283.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jiangpeng Rong
    • 1
  • Shiyao Huang
    • 1
  • Zeyu Shang
    • 1
  • Xianghua Ying
    • 1
    Email author
  1. 1.Key Laboratory of Machine Perception (Ministry of Education), School of Electronic Engineering and Computer SciencePeking UniversityBeijingPeople’s Republic of China

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