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  • Received: Mar. 16, 2020

    Accepted: Jun. 11, 2020

    Posted: Dec. 1, 2020

    Published Online: Nov. 18, 2020

    The Author Email: Xu Jie (1141849828@qq.com)

    DOI: 10.3788/LOP57.241504

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    Wenyi Chen, Jie Xu, Hui Yang, Xiaobao Yang, Xiaoqiang Xi. Distortion Correction of Single Image Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241504

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Laser & Optoelectronics Progress, Vol. 57, Issue 24, 241504 (2020)

Distortion Correction of Single Image Based on Deep Learning

Chen Wenyi1, Xu Jie1,*, Yang Hui1, Yang Xiaobao2, and Xi Xiaoqiang2

Author Affiliations

  • 1Institute of Internet of Things and Integration of IT Application and Industrialization, Xi'an University of Posts & Telecommunications, Xi'an, Shannxi 710061, China;
  • 2College of Communication and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an, Shannxi 710121, China;

Abstract

For the convenience and applicability of distortion correction methods, a distortion correction method based on convolutional neural networks is presented in this paper. First, the self-calibration functional motion structure is used to reconstruct the image sequence taken by the real camera to estimate the camera parameters. Second, according to the functional relationship between the first and second-order radial distortion parameters, the images within the common radial distortion range are generated to solve the problem of less distorted images with the first and second-order radial distortion annotation. Finally, by using the powerful learning ability of CNN, the radial distortion features are learned to estimate the radial deformation, and the input image is mapped to the distortion coefficient to realize the image distortion correction. Experimental results show that the calibration error of this method is about 1 pixel compared with the traditional camera calibration method.

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