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Volume 46 Issue 4
Apr.  2024
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SHI Yifeng, CHEN Nan, ZHU Fang, MAO Wenbiao, LI Faming, WANG Tianfu, ZHANG Jiqing, YAO Libin. Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes[J]. Infrared Technology , 2024, 46(4): 427-436.
Citation: SHI Yifeng, CHEN Nan, ZHU Fang, MAO Wenbiao, LI Faming, WANG Tianfu, ZHANG Jiqing, YAO Libin. Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes[J]. Infrared Technology , 2024, 46(4): 427-436.
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Single-frame Infrared Image Super-Resolution Reconstruction for Real Scenes

  • Kunming Institute of Physics, Kunming 650223, China

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  • Received Date: December 05, 2023
  • Revised Date: January 18, 2024
    Graphical Abstract
    • Abstract
  • Current infrared image super-resolution reconstruction methods, which are primarily designed based on experimental data, often fail in complex degradation scenarios encountered in real-world environments. To address this challenge, this paper presents a novel deep learning-based approach tailored for the super-resolution reconstruction of infrared images in real scenarios. The significant contributions of this research include the development of a model that simulates infrared image degradation in real-life settings and a network structure that integrates channel attention with dense connections. This structure enhances feature extraction and image reconstruction capabilities, effectively increasing the spatial resolution of low-resolution infrared images in realistic scenarios. The effectiveness and superiority of the proposed approach for processing infrared images in real-world contexts are demonstrated through a series of ablation studies and comparative experiments with existing super-resolution methods. The experimental results indicate that this method produces sharper edges and effectively eliminates noise and blur, thereby significantly improving the visual quality of the images.
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    • References (27)
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