Direct prejudgement of hot images with detected diffraction rings in high power laser system

Aihua Yang; Zhan Li; Dean Liu; Jie Miao; Jianqiang Zhu

doi:doi:10.1017/hpl.2018.47

High Power Laser Science and Engineering, 2018, 6 (3): 03000e52, Published Online: Sep. 26, 2018

Direct prejudgement of hot images with detected diffraction rings in high power laser system Download： 592次

Aihua Yang ^1,2Zhan Li ^1,2Dean Liu ¹Jie Miao ¹Jianqiang Zhu ¹

Author Affiliations

¹ Joint Laboratory on High Power Laser and Physics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China

² University of Chinese Academy of Sciences, Beijing 100049, China

Figures & Tables

Fig. 1. Schematic diagram of the hot-image formation process.

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Fig. 2. Locations and peak intensities of hot images. (a) Two hot-image planes are both located at ; (b) the extracted intensity distributions and lateral positions of hot images.

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Fig. 3. Evolution of gray value of the diffraction ring and the luminance disk. (a) Diffraction ring image; (b) luminance disk (matching template).

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Fig. 4. Gradient direction fields used to indicate the evolution of gray value. (a) Gradient direction field of the diffraction ring; (b) gradient direction field of the luminance disk.

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Fig. 5. Flow chart of the GDM method.

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Fig. 6. Simulated diffraction rings and the recognition results. (a)–(d) Simulated diffraction rings with different overlap degrees; (e)–(h) similarity distributions and the detected centers.

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Fig. 7. Experimental setup for the diffraction optical path that uses SLM to simulate a defect.

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Fig. 8. Diffraction ring images and similarity distributions. (a)–(e) Diffraction ring images with different compression ratios; (f)–(j) similarity distributions and the detected diffraction center labeled with a red circle.

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Fig. 9. Diffraction ring image and similarity distributions. (a) The collected diffraction ring image at 41 cm; (b) similarity distribution with six initially detected centers; (c) similarity distribution with two centers left after cluster analysis.

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Aihua Yang, Zhan Li, Dean Liu, Jie Miao, Jianqiang Zhu. Direct prejudgement of hot images with detected diffraction rings in high power laser system[J]. High Power Laser Science and Engineering, 2018, 6(3): 03000e52.

Direct prejudgement of hot images with detected diffraction rings in high power laser system Download： 592次

Fig. 1. Schematic diagram of the hot-image formation process.

Fig. 2. Locations and peak intensities of hot images. (a) Two hot-image planes are both located at ; (b) the extracted intensity distributions and lateral positions of hot images.

Fig. 3. Evolution of gray value of the diffraction ring and the luminance disk. (a) Diffraction ring image; (b) luminance disk (matching template).

Fig. 4. Gradient direction fields used to indicate the evolution of gray value. (a) Gradient direction field of the diffraction ring; (b) gradient direction field of the luminance disk.

Fig. 5. Flow chart of the GDM method.

Fig. 6. Simulated diffraction rings and the recognition results. (a)–(d) Simulated diffraction rings with different overlap degrees; (e)–(h) similarity distributions and the detected centers.

Fig. 7. Experimental setup for the diffraction optical path that uses SLM to simulate a defect.

Fig. 8. Diffraction ring images and similarity distributions. (a)–(e) Diffraction ring images with different compression ratios; (f)–(j) similarity distributions and the detected diffraction center labeled with a red circle.

Fig. 9. Diffraction ring image and similarity distributions. (a) The collected diffraction ring image at 41 cm; (b) similarity distribution with six initially detected centers; (c) similarity distribution with two centers left after cluster analysis.

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Direct prejudgement of hot images with detected diffraction rings in high power laser system Download： 592次

Fig. 1. Schematic diagram of the hot-image formation process.

Fig. 2. Locations and peak intensities of hot images. (a) Two hot-image planes are both located at ; (b) the extracted intensity distributions and lateral positions of hot images.

Fig. 3. Evolution of gray value of the diffraction ring and the luminance disk. (a) Diffraction ring image; (b) luminance disk (matching template).

Fig. 4. Gradient direction fields used to indicate the evolution of gray value. (a) Gradient direction field of the diffraction ring; (b) gradient direction field of the luminance disk.

Fig. 5. Flow chart of the GDM method.

Fig. 6. Simulated diffraction rings and the recognition results. (a)–(d) Simulated diffraction rings with different overlap degrees; (e)–(h) similarity distributions and the detected centers.

Fig. 7. Experimental setup for the diffraction optical path that uses SLM to simulate a defect.

Fig. 8. Diffraction ring images and similarity distributions. (a)–(e) Diffraction ring images with different compression ratios; (f)–(j) similarity distributions and the detected diffraction center labeled with a red circle.

Fig. 9. Diffraction ring image and similarity distributions. (a) The collected diffraction ring image at 41 cm; (b) similarity distribution with six initially detected centers; (c) similarity distribution with two centers left after cluster analysis.

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