Acta Optica Sinica, Volume. 39, Issue 3, 0304001(2019)

Super-Resolution Algorithm Based on Precise Color Vector Constraint of Light Field Camera

Fusheng Sun* and Xie Han
Author Affiliations
  • School of Data Science and Technology, North University of China, Taiyuan, Shanxi 030051, China
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    Figures & Tables(18)
    Principle diagram of optical field imaging
    Measured results of PSF of LYTRO camera
    Hexagonal RST coordinate system
    Distribution and location diagram of diffusion function points (a) and local magnification diagram (b)
    Distribution of color filters and diffusion function points
    Pyramid model
    Pyramid algorithm model of LYTRO camera
    Algorithm flow chart
    Original image information collected by LYTRO camera. (a) Image 1; (b) image 2
    Experimental results and local enlargement of 4 algorithms
    Color recovery results of two sets of images. (a) Bilinear algorithm; (b) Lu's algorithm; (c) adaptive algorithm; (d) proposed algorithm
    Original image and sub-aperture images. (a) Original image; (b) sub-aperture image; (c) 7×7 sub-aperture images
    Original image and local magnified images. (a) Original image; (b) local magnification image 1; (c) local magnification image 2; (d) local two times enlarged image 3
    Experimental comparison of algorithms. (a) Toolbox W/O rectification+dual three-time on-sample; (b) toolbox W/O rectification+super-resolution; (c) document algorithm[25]; (d) proposed algorithm
    • Table 1. Diffusion function point coordinates of different layers

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      Table 1. Diffusion function point coordinates of different layers

      Point1234
      a(0,11.671)(0,23.758)(0,35.239)(0,46.837)
      b(10.107,5.836)(20.575,11.879)(30.518,17.620)(40.562,23.419)
      c(10.107,-5.836)(20.575,-11.879)(30.518,-17.620)(40.562,-23.419)
      d(0,-11.671)(0,-23.758)(0,-35.239)(0,-46.837)
      e(-10.701,-5.836)(-20.575,-11.879)(-30.518,-17.620)(-40.562,-23.419)
      f(-10.701,5.836)(-20.575,11.879)(-30.518,17.620)(-40.562,23.419)
    • Table 2. Color filter distribution mode

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      Table 2. Color filter distribution mode

      Column No.Mode
      1RGrRGrRR
      2GbBGbBGbGb
      3RGrRGrRR
      4GbBGbBGbGb
      5RGrRGrRR
      NGbBGbBGbGb
    • Table 3. Image 1 evaluation data

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      Table 3. Image 1 evaluation data

      Image 1Mean square error ERMean square error EGMean square error EBRunning time /sSignal-to-noise ratio /dB
      Bilinear method326.3563120.8871335.10953.782325.6157
      Adaptive method36.225621.156530.36893.5177635.4862
      Lu's method23.369713.163920.3547260.695340.5687
      Proposed method6.415410.47818.69714.3192642.2369
    • Table 4. Image 2 evaluation data

      View table

      Table 4. Image 2 evaluation data

      Image 2Mean square error ERMean square error EGMean square error EBRunning time /sSignal-to-noise ratio /dB
      Bilinear method186.477183.9741173.01632.063123.5872
      Adaptive method51.358821.367423.48561.69630730.3547
      Lu's method30.768111.478316.1031109.587533.6971
      Proposed method19.387521.683314.31293.632736.3618
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    Fusheng Sun, Xie Han. Super-Resolution Algorithm Based on Precise Color Vector Constraint of Light Field Camera[J]. Acta Optica Sinica, 2019, 39(3): 0304001

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    Paper Information

    Category: Detectors

    Received: Aug. 28, 2018

    Accepted: Oct. 18, 2018

    Published Online: May. 10, 2019

    The Author Email:

    DOI:10.3788/AOS201939.0304001

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