CUI Yumin, YIN Liju, SUI Liguo, ZHOU Hui, DENG Yulin. Compression and reconstruction of photon counting integral imaging[J]. Journal of Applied Optics, 2023, 44(2): 295-306. DOI: 10.5768/JAO202344.0202001
Citation: CUI Yumin, YIN Liju, SUI Liguo, ZHOU Hui, DENG Yulin. Compression and reconstruction of photon counting integral imaging[J]. Journal of Applied Optics, 2023, 44(2): 295-306. DOI: 10.5768/JAO202344.0202001
shu

Compression and reconstruction of photon counting integral imaging

  • 1.

    School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255049, China

  • 2.

    Weifang Engineering Vocational College, Weifang 262500, China

More Information
  • Received Date: December 07, 2022
  • Revised Date: February 02, 2023
  • Available Online: February 09, 2023
    Graphical Abstract
    • Abstract

  • As an important naked eyes three-dimensional display technology, integral imaging technology completely records the three-dimensional scene information while the huge amount of data puts pressure on the transmission and storage. In order to achieve effective compression and reconstruction of images, according to the characteristics of photon counting integral imaging, a scheme for image compression and reconstruction based on the distributed compressed sensing theory was proposed. In this scheme, images were divided into reference images and non-reference images, which were set with different measurement rates and reconstructed respectively. To ensure the reconstruction quality of non-reference images, a joint reconstruction algorithm was proposed. Firstly, the block measurements were performed on the non-reference images, and the image blocks classification was carried out by considering the correlation with the reference images. Then, combined the measurement information of the reference images, a new measurement vector was constructed to complete the initial image reconstruction. In order to further improve the image reconstruction quality, the image was reconstructed with secondary residual compensation to obtain the final reconstruction results. Finally, a large number of experiments were conducted by setting different measurement rates. The experimental results show that the image reconstruction quality of the proposed algorithm can reach 30 dB when the measurement rate is 0.25, and can reach 35 dB when the measurement rate is 0.4. The performance of the algorithm has certain superiority.

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