Volume 13 Issue 6
Dec.  2020
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Chinese Optics  > 2020  >  13(6): 1332-1342.
ZHANG Shi-lei, CUI Yu, XING Mu-zeng, YAN Bin-bin. Light field imaging target ranging technology[J]. Chinese Optics, 2020, 13(6): 1332-1342. doi: 10.37188/CO.2020-0043
Citation: ZHANG Shi-lei, CUI Yu, XING Mu-zeng, YAN Bin-bin. Light field imaging target ranging technology[J]. Chinese Optics, 2020, 13(6): 1332-1342. doi: 10.37188/CO.2020-0043
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Light field imaging target ranging technology

  • 1.

    Chinese People's Liberation Army 92941, Huludao 125001, China

  • 2.

    Shanghai Institute of Aerospace Technology, Shanghai 201100, China

  • 3.

    School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China

Funds:  Supported by the Joint Fund of the National Natural Science Commission and China Academy of Engineering physics (No. U1730135)
More Information
  • Corresponding author: yanbinbin@nwpu.edu.cn
  • Received Date: 20 Mar 2020
  • Rev Recd Date: 24 Apr 2020
    • Available Online: 10 Nov 2020
  • Publish Date: 01 Dec 2020
    Abstract
  • At present, it is difficult to obtain target distance information in image guidance. In order to apply modern guidance laws to image guidance technology and improve its performance, a target ranging algorithm using light field imaging is proposed. The algorithm decodes and tunes light field data to extract sub-aperture images from an original image. Bilinear interpolation is then performed on the two sub-aperture images to improve the image’s spatial resolution, and two sub-aperture images are selected as calibration data to obtain the corresponding internal and external parameters. The parameters are used to correct the sub-aperture images, which aligns them and makes them coplanar. Finally, a semi-global matching method is used to match the images to obtain the disparity value of the target. Then, 3D transformation of parallax can be used to get the target distance. The experimental results show that the average measurement errors of the algorithm are 28.54 mm and 14.96 mm, respectively, before and after improvement. This algorithm can effectively extract target distance information in complex scenes, which has value in theoretical and real-world applications.

     

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