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Laser & Optoelectronics Progress, Volume. 60, Issue 24, 2410012(2023)

Curved Texture Flattening Algorithm Based on the Light Field Camera

Shengnan Qin1,2,3 and Yanting Lu1,2、*
Author Affiliations
  • 1Nanjing Institute of Astronomical Optics & Technology, Chinese Academy of Sciences, Nanjing 210042, Jiangsu, China
  • 2CAS Key Laboratory of Astronomical Optics & Technology, Nanjing Institute of Astronomical Optics & Technology, Nanjing 210042, Jiangsu, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (15)
    Equations (5)
    References (26)
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    Figures & Tables(15)
    Flow chart of the curved texture flattening algorithm

    Fig. 1. Flow chart of the curved texture flattening algorithm

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    Schematic of local texture distortion correction

    Fig. 2. Schematic of local texture distortion correction

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    Simulated output of light field camera for the curved characters texture. (a) Texture image; (b) depth map

    Fig. 3. Simulated output of light field camera for the curved characters texture. (a) Texture image; (b) depth map

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    Surface segmentation. (a) Cluster evaluation value; (b) cluster result; (c) local texture images before and after dilation

    Fig. 4. Surface segmentation. (a) Cluster evaluation value; (b) cluster result; (c) local texture images before and after dilation

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    Correction results of local textures. (a)‒(d) Local textures before correction; (e)‒(h) local textures after correction

    Fig. 5. Correction results of local textures. (a)‒(d) Local textures before correction; (e)‒(h) local textures after correction

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    Image stitching. (a) Stitching of the local texture images in Fig. 5; (b) texture flattening result of the whole curved surface

    Fig. 6. Image stitching. (a) Stitching of the local texture images in Fig. 5; (b) texture flattening result of the whole curved surface

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    Flattening results of different texture images. (a)‒(c) Original texture images of fingerprint, chinaware and mural; (d)‒(f) texture images of fingerprint, chinaware and mural on curved surface; (g)‒(i) flattening results of curved fingerprint, chinaware and mural

    Fig. 7. Flattening results of different texture images. (a)‒(c) Original texture images of fingerprint, chinaware and mural; (d)‒(f) texture images of fingerprint, chinaware and mural on curved surface; (g)‒(i) flattening results of curved fingerprint, chinaware and mural

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    Texture flattening experiment based on the focused light field camera. (a) Curved finger image output by light field camera; (b) depth map output by light field camera; (c) flattening result of the curved finger image

    Fig. 8. Texture flattening experiment based on the focused light field camera. (a) Curved finger image output by light field camera; (b) depth map output by light field camera; (c) flattening result of the curved finger image

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    Flattening results for textures on surface with different degrees of bending (the image at the lower-right corner is a close-up of the region labeled by rectangular box). (a)‒(c) Texture images on curved surface with different degrees of bending; (d)‒(f) corresponding texture flattening results

    Fig. 9. Flattening results for textures on surface with different degrees of bending (the image at the lower-right corner is a close-up of the region labeled by rectangular box). (a)‒(c) Texture images on curved surface with different degrees of bending; (d)‒(f) corresponding texture flattening results

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    Flattening results of noisy texture images with different contrasts (the image at lower-right corner is a close-up of the region labeled by the rectangular box). (a)‒(d) Curved texture images with 0.2‒0.5 contrasts; (e)‒(h) corresponding texture flattening results (contrasts of 0.2‒0.5); (i)‒(l) curved texture images with 0.6‒0.9 contrasts; (m)‒(p) corresponding texture flattening results (contrasts of 0.6‒0.9)

    Fig. 10. Flattening results of noisy texture images with different contrasts (the image at lower-right corner is a close-up of the region labeled by the rectangular box). (a)‒(d) Curved texture images with 0.2‒0.5 contrasts; (e)‒(h) corresponding texture flattening results (contrasts of 0.2‒0.5); (i)‒(l) curved texture images with 0.6‒0.9 contrasts; (m)‒(p) corresponding texture flattening results (contrasts of 0.6‒0.9)

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    Perspective correction results of the local texture images with depth noise of different standard deviations. (a) Texture image (the region labeled with rectangular box is the region to be corrected); (b) σ=0.001 mm; (c) σ=0.003 mm; (d) σ=0.005 mm; (e) σ=0.007 mm

    Fig. 11. Perspective correction results of the local texture images with depth noise of different standard deviations. (a) Texture image (the region labeled with rectangular box is the region to be corrected); (b) σ=0.001 mm; (c) σ=0.003 mm; (d) σ=0.005 mm; (e) σ=0.007 mm

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    Texture image and its flattening result when its depth map is superimposed with 0.005 mm Gaussian noises. (a) Texture image (the region labeled with rectangular box is the local texture selection range); (b) flattening result

    Fig. 12. Texture image and its flattening result when its depth map is superimposed with 0.005 mm Gaussian noises. (a) Texture image (the region labeled with rectangular box is the local texture selection range); (b) flattening result

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    Correction results of local texture images with different normal vectors. (a)~(e) Local textures to be corrected; (f)~(j) correction results of local texture images without depth noise; (k)~(o) correction results of local texture images with depth noise of the same standard deviation

    Fig. 13. Correction results of local texture images with different normal vectors. (a)~(e) Local textures to be corrected; (f)~(j) correction results of local texture images without depth noise; (k)~(o) correction results of local texture images with depth noise of the same standard deviation

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    • Table 1. Quantitative evaluation values of flattening results of noisy texture images with different contrasts

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      Table 1. Quantitative evaluation values of flattening results of noisy texture images with different contrasts

      Image contrast0.20.30.40.50.60.70.80.9
      VNCC,max0.7290.7320.8100.8330.8500.8570.8640.900

    • Table 2. Influences of the depth noise on correction of local texture images with different normal vectors

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      Table 2. Influences of the depth noise on correction of local texture images with different normal vectors

      Areaabcde
      θ /（°）06.99214.41022.76732.766
      θ' /（°）0.0156.97814.42222.79432.794
      AE /（°）0.0150.0140.0120.0270.028
      VNCC,max0.9860.9570.9540.9490.932
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    Shengnan Qin, Yanting Lu. Curved Texture Flattening Algorithm Based on the Light Field Camera[J]. Laser & Optoelectronics Progress, 2023, 60(24): 2410012

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    Paper Information

    Category: Image Processing

    Received: Mar. 23, 2023

    Accepted: May. 15, 2023

    Published Online: Nov. 27, 2023

    The Author Email: Lu Yanting (ytlu@niao.ac.cn)

    DOI:10.3788/LOP230937

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology