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Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle

Author

Listed:
  • ZhengQiang Xiong
  • Qiuze Yu
  • Tao Sun
  • Wen Chen
  • Yuhao Wu
  • Jie Yin

Abstract

Super-resolution (SR) technology provides a far promising computational imaging approach in obtaining a high-resolution (HR) image (or image sequences) from observed multiple low-resolution (LR) images by incorporating complementary information. In this paper, a three-stage SR method is proposed to generate a HR image from infrared (IR) LR Images acquired with Unmanned Aerial Vehicle (UAV). The proposed method integrates a high-level image capturing process and a low-level SR process. In this integrated process, we incorporate UAV path optimization, sub-pixel image registration, and sparseness constraint into a computational imaging framework of a region of interest (ROI). To refine ROI complementary feathers, we design an optimal flight control scheme to acquire adequate image sequences from multi-angles. In particular, a phase correlation approach achieving reliable sub-pixel image feature matching is adapted, on the basis of which an effective sparseness regularization model is built to enhance the fine structures of the IR image. Unlike most traditional multiple-frame SR algorithms that mainly focus on signal processing and achieve good performances when using standard test datasets, the performed experiments with real-life IR sequences indicate the three-stage SR method can also deal with practical LR IR image sequences collected by UAVs. The experimental results demonstrate that the proposed method is capable of generating HR images with good performance in terms of edge preservation and detail enhancement.

Suggested Citation

  • ZhengQiang Xiong & Qiuze Yu & Tao Sun & Wen Chen & Yuhao Wu & Jie Yin, 2020. "Super-resolution reconstruction of real infrared images acquired with unmanned aerial vehicle," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
  • Handle: RePEc:plo:pone00:0234775
    DOI: 10.1371/journal.pone.0234775
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    References listed on IDEAS

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    1. He Luo & Zhengzheng Liang & Moning Zhu & Xiaoxuan Hu & Guoqiang Wang, 2018. "Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-24, March.
    2. Kai Yit Kok & Parvathy Rajendran, 2016. "Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    3. Xiaoyu Shi & Galo Garcia III & Yina Wang & Jeremy F Reiter & Bo Huang, 2019. "Deformed alignment of super-resolution images for semi-flexible structures," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-12, March.
    4. Tomoyuki Obuchi & Shiro Ikeda & Kazunori Akiyama & Yoshiyuki Kabashima, 2017. "Accelerating cross-validation with total variation and its application to super-resolution imaging," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-14, December.
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