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Laser & Optoelectronics Progress, Volume. 56, Issue 4, 041001(2019)

Quality Assessment of Hyperspectral Super-Resolution Images

Song Xue1, Siyu Zhang2、**, and Yongfeng Liu1、*
Author Affiliations
  • 1 Department of Weapons Engineering, Army Academy of Artillery and Air Defense, Hefei, Anhui 230000, China
  • 2 Postgraduate Team 1, Army Academy of Artillery and Air Defense, Hefei, Anhui 230000, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
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    References (23)
    Cited By (2)
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    Figures & Tables(10)
    Super-resolution reconstruction effects of 25th, 50th, 75th and 100th band images. (a) Hyperspectral image; (b) hyperspectral super-resolution image; (c) scene image blocks for hyperspectral image; (d) scene image blocks for hyperspectral super-resolution image

    Fig. 1. Super-resolution reconstruction effects of 25th, 50th, 75th and 100th band images. (a) Hyperspectral image; (b) hyperspectral super-resolution image; (c) scene image blocks for hyperspectral image; (d) scene image blocks for hyperspectral super-resolution image

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    Statistical regularity of MSCN

    Fig. 2. Statistical regularity of MSCN

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    Extraction method of adjacent factors

    Fig. 3. Extraction method of adjacent factors

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    Statistic of adjacent factors along four directions. (a) Horizontal direction; (b) vertical direction; (c) main-diagonal direction; (d) secondary-diagonal direction

    Fig. 4. Statistic of adjacent factors along four directions. (a) Horizontal direction; (b) vertical direction; (c) main-diagonal direction; (d) secondary-diagonal direction

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    GLBP feature maps of hyperspectral super-resolution images. (a) 10th band; (b) 20th band; (c) 30th band; (d) 40th band; (e) 50th band; (f) 60th band; (g) 70th band; (h) 80th band; (i) 90th band; (j) 100th band

    Fig. 5. GLBP feature maps of hyperspectral super-resolution images. (a) 10th band; (b) 20th band; (c) 30th band; (d) 40th band; (e) 50th band; (f) 60th band; (g) 70th band; (h) 80th band; (i) 90th band; (j) 100th band

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    Flow chart of algorithm model

    Fig. 6. Flow chart of algorithm model

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    Scatter plots by different models. (a) BLIINDS-II; (b) QAC; (c) BRISQUE; (d) NIQE; (e) proposed algorithm

    Fig. 7. Scatter plots by different models. (a) BLIINDS-II; (b) QAC; (c) BRISQUE; (d) NIQE; (e) proposed algorithm

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    • Table 1. Algorithm test results of all algorithms

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      Table 1. Algorithm test results of all algorithms

      ParameterBLIINDS-IIQACBRISQUENIQEProposed algorithm
      SROCC0.31940.47470.67380.49000.8412
      PLCC0.38760.30970.71720.55800.8763

    • Table 2. Subjective-objective assessment results for 14 groups of image libraries

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      Table 2. Subjective-objective assessment results for 14 groups of image libraries

      ParameterImagelibrary 1Imagelibrary 2Imagelibrary 3Imagelibrary 4Imagelibrary 5Imagelibrary 6Imagelibrary 7
      SROCC0.80320.81630.81960.83910.83440.83150.8367
      PLCC0.81660.81460.83430.85670.83730.84620.8596
      ParameterImagelibrary 8Imagelibrary 9Imagelibrary 10Imagelibrary 11Imagelibrary 12Imagelibrary 13Imagelibrary 14
      SROCC0.84180.84980.83110.83230.83780.82880.8216
      PLCC0.86210.86430.85370.84560.85270.83620.8354

    • Table 3. Runtime of all algorithmss

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      Table 3. Runtime of all algorithmss

      AlgorithmBLIINDS-IIQACBRISQUENIQEProposed algorithm
      Time42.30.20.30.525.6
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    Song Xue, Siyu Zhang, Yongfeng Liu. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001

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    Paper Information

    Category: Image Processing

    Received: Aug. 23, 2018

    Accepted: Aug. 31, 2018

    Published Online: Jul. 31, 2019

    The Author Email: Zhang Siyu (yusonzhang@foxmail.com), Liu Yongfeng (954271756@qq.com)

    DOI:10.3788/LOP56.041001

    Topics

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