• Acta Optica Sinica
  • Vol. 39, Issue 11, 1111001 (2019)
Hao Wei1、2, Haihua Cui1、2、*, Xiaosheng Cheng1、2, and Xiaodi Zhang1、2
Author Affiliations
  • 1College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China
  • 2Research Center of Digital Design and Manufacturing Engineering Technology of Jiangsu Province, Nanjing, Jiangsu 210016, China
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    DOI: 10.3788/AOS201939.1111001 Cite this Article Set citation alerts
    Hao Wei, Haihua Cui, Xiaosheng Cheng, Xiaodi Zhang. Image Defocus Simulation Technology Applied to Evaluation of Focused Morphology Recovery Algorithm[J]. Acta Optica Sinica, 2019, 39(11): 1111001 Copy Citation Text show less
    Principle of focused morphology recovery
    Fig. 1. Principle of focused morphology recovery
    Point sampling process
    Fig. 2. Point sampling process
    Algorithm process of transforming single triangle patch to points
    Fig. 3. Algorithm process of transforming single triangle patch to points
    Process of lens imaging
    Fig. 4. Process of lens imaging
    Imaging plane coincides with image detector plane
    Fig. 5. Imaging plane coincides with image detector plane
    Imaging plane locates in front of image detector plane
    Fig. 6. Imaging plane locates in front of image detector plane
    Imaging plane locates in back of image detector plane
    Fig. 7. Imaging plane locates in back of image detector plane
    Calculation of simulation images
    Fig. 8. Calculation of simulation images
    Texture image and simulated results. (a) Texture image; (b) simulated image; (c) simulated sequence images
    Fig. 9. Texture image and simulated results. (a) Texture image; (b) simulated image; (c) simulated sequence images
    Two complex models and corresponding simulated images. (a) Model A; (b) model B; (c) simulated images of model A; (d) simulated images of model B
    Fig. 10. Two complex models and corresponding simulated images. (a) Model A; (b) model B; (c) simulated images of model A; (d) simulated images of model B
    Grayscale images of focusing evaluation
    Fig. 11. Grayscale images of focusing evaluation
    Depth maps obtained by different operators. (a) GLV operator; (b) SML operator
    Fig. 12. Depth maps obtained by different operators. (a) GLV operator; (b) SML operator
    Comparison of root mean square error
    Fig. 13. Comparison of root mean square error
    Hao Wei, Haihua Cui, Xiaosheng Cheng, Xiaodi Zhang. Image Defocus Simulation Technology Applied to Evaluation of Focused Morphology Recovery Algorithm[J]. Acta Optica Sinica, 2019, 39(11): 1111001
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