Chinese Journal of Lasers, Volume. 50, Issue 18, 1809001(2023)

Brightness Enhancement Algorithm for Infrared Digital Holographic Image Through Smoke

Danlu Zhao1,2, Yongan Zhang1,2、*, Guanghui He1,2, Junhao Huang1,2, and Yaping Zhang1,2
Author Affiliations
  • 1Faculty of Science, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
  • 2Yunnan Provincial Key Laboratory of Modern Information Optics, Kunming University of Science and Technology, Kunming 650500, Yunnan, China
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    Figures & Tables(12)
    Mach-Zehnder interferometric optical setup with optical path through smoke
    Simulation of smoke imaging effect. (a) Object to be imaged; (b) imaging through smoke
    Holograms and reconstructed images. (a) Smoke-free hologram; (b) through-smoke hologram; (c) reconstructed image without smoke; (d) reconstructed image through smoke field; (e) smoke-free -1 order image extraction result; (f) through-smoke -1 order image extraction result
    Algorithm flow chart
    Reconstructed image denoising. (a) BM3D denoising; (b) bilateral filtering denoising
    Target object extraction result. (a) Target object area; (b) segmented target object
    Results of shaded area segmentation. (a) Edge processing by Sobel operator; (b) image expansion; (c) image filling; (d) segmentation of shaded area
    Tent‐SSA algorithm flow chart
    Comparison between SSA algorithm and Tent‐SSA algorithm. (a) Convergence plot of objective function; (b) brightness deviation curve
    Processing results of proposed algorithm. (a) t=8 s reconstructed image; (b) t=10 s reconstructed image; (c) t=12 s reconstructed image; (d) t=8 s enhanced image; (e) t=10 s enhanced image; (f) t=12 s enhanced image; (g) t=14 s reconstructed image; (h) t=16 s reconstructed image; (i) t=18 s reconstructed image; (j) t=14 s enhanced image; (k) t=16 s enhanced image; (l) t=18 s enhanced image
    Comparison of effects of four algorithms. (a) t=8 s, algorithm in Ref. [19]; (b) t=8 s, algorithm in Ref. [22]; (c) t=8 s, CLAHE algorithm; (d) t=8 s, algorithm in this paper; (e) t=10 s, algorithm in Ref. [19]; (f) t=10 s, algorithm in Ref. [22]; (g) t=10 s, CLAHE algorithm; (h) t=10 s, algorithm in this paper; (i) t=12 s, algorithm in Ref. [19]; (j) t=12 s, algorithm in Ref. [22]; (k) t=12 s, CLAHE algorithm; (l) t=12 s, algorithm in this paper
    • Table 1. Comparison of image quality evaluation parameters

      View table

      Table 1. Comparison of image quality evaluation parameters

      Image sampleEvaluation indicator

      Algorithm in

      Ref.[19

      Algorithm in

      Ref.[22

      CLAHEProposed algorithm
      t=8 sPSNR/dB12.847214.226613.060322.1124
      MSE3375.66712457.09653214.0567399.8019
      FSIM0.66760.72260.77710.9521
      t=10 sPSNR/dB12.838014.275913.009022.2017
      MSE3382.85402429.33313252.2421391.6589
      FSIM0.67720.72670.78090.9511
      t=12 sPSNR/dB12.162814.907312.434621.9866
      MSE3951.80682100.61203712.1315411.5443
      FSIM0.69460.75320.77810.9615
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    Danlu Zhao, Yongan Zhang, Guanghui He, Junhao Huang, Yaping Zhang. Brightness Enhancement Algorithm for Infrared Digital Holographic Image Through Smoke[J]. Chinese Journal of Lasers, 2023, 50(18): 1809001

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    Paper Information

    Category: holography and information processing

    Received: Oct. 11, 2022

    Accepted: Dec. 16, 2022

    Published Online: Aug. 29, 2023

    The Author Email: Zhang Yongan (1295720542@qq.com)

    DOI:10.3788/CJL221316

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