• Acta Optica Sinica
  • Vol. 40, Issue 1, 0111027 (2020)
Wentong Qian1、2, hui Li1、2、3、*, and Yuntao Wu1、2
Author Affiliations
  • 1School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei 430205, China
  • 2Hubei Key Laboratory of Intelligent Robot, Wuhan, Hubei 430205, China
  • 3School of Chemistry and Chemical Engineering, Huazhong University of Science & Technology, Wuhan, Hubei 430074, China;
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    DOI: 10.3788/AOS202040.0111027 Cite this Article Set citation alerts
    Wentong Qian, hui Li, Yuntao Wu. Synthetic-Aperture Occlusion Removal Algorithm Using Microlens Array[J]. Acta Optica Sinica, 2020, 40(1): 0111027 Copy Citation Text show less
    Flow chart of synthetic-aperture occlusion removal algorithm based on microlens array
    Fig. 1. Flow chart of synthetic-aperture occlusion removal algorithm based on microlens array
    Light field sampling based on microlens array
    Fig. 2. Light field sampling based on microlens array
    Principle of refocusing
    Fig. 3. Principle of refocusing
    Scene diagram
    Fig. 4. Scene diagram
    Original image
    Fig. 5. Original image
    Processing images of algorithm for each step. (a) Sampling image of microlens; (b) binary extracted occlusion grid; (c) result without using synthetic aperture; (d) occlusion removal result
    Fig. 6. Processing images of algorithm for each step. (a) Sampling image of microlens; (b) binary extracted occlusion grid; (c) result without using synthetic aperture; (d) occlusion removal result
    Results of three algorithms. (a) Microlens synthetic aperture algorithm; (b) multi-cue fusion extraction depth algorithm; (c) prior information matching based algorithm
    Fig. 7. Results of three algorithms. (a) Microlens synthetic aperture algorithm; (b) multi-cue fusion extraction depth algorithm; (c) prior information matching based algorithm
    Images processed by three algorithms. (a)-(c) Binarization images of Figs. 7(a)-(c); (d)-(f) contour extraction images of Figs. 7(a)-(c)
    Fig. 8. Images processed by three algorithms. (a)-(c) Binarization images of Figs. 7(a)-(c); (d)-(f) contour extraction images of Figs. 7(a)-(c)
    PitchRadius of curvatureFocal lengthPupil
    0.15 mm2.4 mm5.2 mmChrome
    Table 1. Parameters of MLA150-5C microlens array
    GroupAlgorithmAveragegradient
    Synthetic aperture of microlens array0.0476
    Group1Multi-cue fusion extraction depth[4]0.0212
    Base on regional prior information[6]0.0182
    Synthetic aperture of microlens array0.0324
    Group2Multi-cue fusion extraction depth[4]0.0238
    Base on regional prior information[6]0.0129
    Synthetic aperture of microlens array0.0387
    Group3Multi-cue fusion extraction depth[4]0.0232
    Base on regional prior information[6]0.0202
    Table 2. Average gradient value of results of three algorithms
    GroupAlgorithmBrennerTenengradSMDSMD2Energy
    Synthetic aperture of microlens array21.638346.2426187.63924.8237102.392
    Group1Multi-cue fusion extraction depth[4]17.2057297.1573121.83294.792382.4373
    Base on regional prior information[6]9.1939240.038183.23983.490842.9048
    Synthetic aperture of microlens array32.814392.8423239.84785.2394124.324
    Group2Multi-cue fusion extraction depth[4]28.932242.7176131.23664.308258.6362
    Base on regional prior information[6]8.2309224.853978.49052.074539.7248
    Synthetic aperture of microlens array27.8409279.804595.73566.013997.4584
    Group3Multi-cue fusion extraction depth[4]15.897239.740182.31475.225361.4399
    Base on regional prior information[6]9.2347134.562963.47594.826957.2825
    Table 3. Comparison of image qualities obtained by different algorithms
    Wentong Qian, hui Li, Yuntao Wu. Synthetic-Aperture Occlusion Removal Algorithm Using Microlens Array[J]. Acta Optica Sinica, 2020, 40(1): 0111027
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