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Acta Optica Sinica, Volume. 42, Issue 10, 1010001(2022)

Dark Channel and Cross Channel Based Multi-Prior Combined Multi-Spectral Super-Resolution Algorithm

Shen Shi1,2,3,4, Zengshan Yin2,4、*, and Long Wang2
Author Affiliations
  • 1Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
  • 2Innovation Academy of Microsatellites of Chinese Academy of Sciences, Shanghai 201203, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
  • 4School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China
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    AbstractGet PDF(in Chinese)
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    Multi-spectral remote sensing imaging degradation model from high resolution images to low resolution images

    Fig. 1. Multi-spectral remote sensing imaging degradation model from high resolution images to low resolution images

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    Statistics for dark channel values of high resolution images and low resolution images

    Fig. 2. Statistics for dark channel values of high resolution images and low resolution images

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    General flow of dark channel and cross channel based multi-prior combined multi-spectral super-resolution algorithm

    Fig. 3. General flow of dark channel and cross channel based multi-prior combined multi-spectral super-resolution algorithm

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    Comparison of reconstruction results of aerial remote sensing data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

    Fig. 4. Comparison of reconstruction results of aerial remote sensing data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

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    Comparison of reconstruction results of aircraft remote sensing data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

    Fig. 5. Comparison of reconstruction results of aircraft remote sensing data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

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    Comparison of reconstruction results of MDSP data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

    Fig. 6. Comparison of reconstruction results of MDSP data. (a) Bicubic; (b) TVSR; (c) CASR; (d) DCSR; (e) CCSR; (f) MPSR

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    Multi-spectral images used in simulation experiment. (a) Original high resolution image; (b) low resolution image with 10 dB signal-to-noise ratio; (c) low resolution image with 25 dB signal-to-noise ratio; (d) low resolution image with 40 dB signal-to-noise ratio; (e) low resolution image with chromatic aberration

    Fig. 7. Multi-spectral images used in simulation experiment. (a) Original high resolution image; (b) low resolution image with 10 dB signal-to-noise ratio; (c) low resolution image with 25 dB signal-to-noise ratio; (d) low resolution image with 40 dB signal-to-noise ratio; (e) low resolution image with chromatic aberration

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    • Table 1. Comparison of PSNRs of super-resolution algorithms with different noises or chromatic aberrations

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      Table 1. Comparison of PSNRs of super-resolution algorithms with different noises or chromatic aberrations

      Test termPSNR /dB
      BicubicTVSRCASRDCSRCCSRMPSR
      10 dB signal-to-noise ratio21.2023.4723.4825.3523.2326.11
      25 dB signal-to-noise ratio23.6330.8230.8330.9531.3232.07
      40 dB signal-to-noise ratio23.7433.3733.8133.0933.1934.42
      Chromatic aberration20.8020.4621.4321.2623.5524.65

    • Table 2. Comparison of SSIMs of super-resolution algorithms with different noises or chromatic aberrations

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      Table 2. Comparison of SSIMs of super-resolution algorithms with different noises or chromatic aberrations

      Test termSSIM
      BicubicTVSRCASRDCSRCCSRMPSR
      10 dB signal-to-noise ratio0.68340.74770.74790.82990.80150.8915
      25 dB signal-to-noise ratio0.87020.94690.94700.95050.96350.9654
      40 dB signal-to-noise ratio0.88130.97840.97980.97790.97960.9819
      Chromatic aberration0.65150.67060.69220.78130.80240.8083
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    Shen Shi, Zengshan Yin, Long Wang. Dark Channel and Cross Channel Based Multi-Prior Combined Multi-Spectral Super-Resolution Algorithm[J]. Acta Optica Sinica, 2022, 42(10): 1010001

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    Paper Information

    Category: Image Processing

    Received: Aug. 20, 2021

    Accepted: Dec. 13, 2021

    Published Online: May. 20, 2022

    The Author Email: Yin Zengshan (yinzs@ microsate.com)

    DOI:10.3788/AOS202242.1010001

    Topics

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