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<p>Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing (BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning (TDBL), which adopts both split Bregman iteration (SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2DroneVehicle datasets.</p>

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Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • Maoji Qiu,
  • Hao Liu

摘要

Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing (BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning (TDBL), which adopts both split Bregman iteration (SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2DroneVehicle datasets.