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Acta Optica Sinica, Volume. 40, Issue 22, 2210003(2020)

Regional Backlight Brightness Extraction Algorithm Based on Deep Learning

Tao Zhang1,2, Qin Zeng1,2、*, Wenli Du1,2, and Hao Wang1,2
Author Affiliations
  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
  • 2Texas Instruments DSP Joint Lab, Tianjin University, Tianjin 300072, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (32)
    Cited By (3)
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    Figures & Tables(11)
    Data measurement method based on local backlight extraction and diagram of system structure

    Fig. 1. Data measurement method based on local backlight extraction and diagram of system structure

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    Flowcharts of dividing methods. (a) Mean plus/minus variance method; (b) percentage dividing method

    Fig. 2. Flowcharts of dividing methods. (a) Mean plus/minus variance method; (b) percentage dividing method

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    Measurement system of local backlight extraction

    Fig. 3. Measurement system of local backlight extraction

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    Test environment of local backlight extraction. (a) Experimental setup; (b) dark environment

    Fig. 4. Test environment of local backlight extraction. (a) Experimental setup; (b) dark environment

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    Diagram of local dimming system based on deep learning

    Fig. 5. Diagram of local dimming system based on deep learning

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    Proposed BENN structure

    Fig. 6. Proposed BENN structure

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    Structure of Res module

    Fig. 7. Structure of Res module

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    Simulation results. (a)(c)(e)(g) Traditional algorithm; (b)(d)(f)(h) proposed algorithm

    Fig. 8. Simulation results. (a)(c)(e)(g) Traditional algorithm; (b)(d)(f)(h) proposed algorithm

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    Results of display system. (a)(c)(e)(g)(i)(k)(m)(o)(q)(s)(u)(w) Traditional algorithm; (b)(d)(f)(h)(j)(l)(n)(p)(r)(t)(v)(x) proposed algorithm

    Fig. 9. Results of display system. (a)(c)(e)(g)(i)(k)(m)(o)(q)(s)(u)(w) Traditional algorithm; (b)(d)(f)(h)(j)(l)(n)(p)(r)(t)(v)(x) proposed algorithm

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    • Table 1. Objective valuation indexes

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      Table 1. Objective valuation indexes

      AlgorithmPSNRSSIMCR
      Proposed method27.96720.97932.9293
      Otsu27.24070.8982.2408
      LUT27.21520.89582.2732
      CDF27.19570.88672.1883
      Average method27.23770.85382.0705

    • Table 2. Experimental results of different networks

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      Table 2. Experimental results of different networks

      MethodPSNRSSIMCR
      BENN27.90720.97932.9293
      BENN-5×5 Conv27.91950.97582.9230
      BENN-R_127.90630.97952.9187
      BENN-5×5 Conv-R_127.73750.96852.9228
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    Tao Zhang, Qin Zeng, Wenli Du, Hao Wang. Regional Backlight Brightness Extraction Algorithm Based on Deep Learning[J]. Acta Optica Sinica, 2020, 40(22): 2210003

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    Paper Information

    Category: Image Processing

    Received: Jun. 15, 2020

    Accepted: Jul. 31, 2020

    Published Online: Oct. 25, 2020

    The Author Email: Qin Zeng (zengqin@tju.edu.cn)

    DOI:10.3788/AOS202040.2210003

    Topics

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