• Infrared and Laser Engineering
  • Vol. 53, Issue 5, 20240049 (2024)
Junfeng Cao1,2,3,4, Qinghai Ding5, Depeng Zou6, Hengjia Qin6, and Haibo Luo1,2,3
Author Affiliations
  • 1Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China
  • 2Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • 3Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
  • 4University of Chinese Academy of Sciences, Beijing 100049, China
  • 5Space Star Technology Co., Ltd., Beijing 100086, China
  • 6The Third Military Representative Office of the Air Force Equipment Department in Shenyang, Shenyang 110016, China
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    DOI: 10.3788/IRLA20240049 Cite this Article
    Junfeng Cao, Qinghai Ding, Depeng Zou, Hengjia Qin, Haibo Luo. Deep learning-based infrared imaging degradation model identification and super-resolution reconstruction[J]. Infrared and Laser Engineering, 2024, 53(5): 20240049 Copy Citation Text show less
    Target image acquisition system
    Fig. 1. Target image acquisition system
    Calibration target
    Fig. 2. Calibration target
    Architecture of blur kernel modeling network
    Fig. 3. Architecture of blur kernel modeling network
    Architecture of super-resolution network
    Fig. 4. Architecture of super-resolution network
    Target images acquired at different working temperatures
    Fig. 5. Target images acquired at different working temperatures
    Blur kernel varies with working temperature and spatial position
    Fig. 6. Blur kernel varies with working temperature and spatial position
    (a) Calibrated blur kernels; (b) Blur kernels predicted by blur kernel modeling network
    Fig. 7. (a) Calibrated blur kernels; (b) Blur kernels predicted by blur kernel modeling network
    Prediction accuracy of blur kernel modeling network
    Fig. 8. Prediction accuracy of blur kernel modeling network
    (a) Real-world image acquisition system; (b) Experimental set up inside the test chamber
    Fig. 9. (a) Real-world image acquisition system; (b) Experimental set up inside the test chamber
    Visual results of different methods on “scene 1” images captured at different working temperature for scale factor 4
    Fig. 10. Visual results of different methods on “scene 1” images captured at different working temperature for scale factor 4
    Visual results of different methods on “scene2” images captured at different working temperature for scale factor 4
    Fig. 11. Visual results of different methods on “scene2” images captured at different working temperature for scale factor 4
    MethodNIQE↓PIQE↓BRISQUE↓
    Bicubic6.09192.84452.300
    IKC5.00585.70651.981
    MANet+RRDB-SFT5.53487.48852.561
    KernelGAN+USRNet4.27079.22250.164
    Ours4.24082.09049.614
    Table 1. Quantitative comparison with other methods with scale factor 4
    Junfeng Cao, Qinghai Ding, Depeng Zou, Hengjia Qin, Haibo Luo. Deep learning-based infrared imaging degradation model identification and super-resolution reconstruction[J]. Infrared and Laser Engineering, 2024, 53(5): 20240049
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