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

Maoji Qiu; Hao Liu

doi:10.1007/s11801-026-4165-6

Optoelectronics Letters ›› 2026, Vol. 22 ›› Issue (2) :98 -104. DOI: 10.1007/s11801-026-4165-6

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

Maoji Qiu ¹
, Hao Liu ¹^,^a

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Abstract

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.

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Maoji Qiu, Hao Liu. Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery. Optoelectronics Letters, 2026, 22(2): 98-104 DOI:10.1007/s11801-026-4165-6

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References

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[1]	Wang W, Chen J H, Zhang Y Fet al. . Adaptive compressive sensing based on sparsity order estimation for wireless image sensor networks. IEEE sensors journal. 2024, 24(13): 21132-21142. J]

[2]	Li C B, Yin W T, Zhang Y. User’s guide for TVAL3: TV minimization by augmented Lagrangian and alternating direction algorithms. CAAM report. 2009, 20(4): 46-47[R]

[3]	Mun S, Fowler J E. Block compressed sensing of images using directional transforms. 16th IEEE International Conference on Image Processing, November 7–10, 2009, Cairo, Egypt. 2009, New York, IEEE30213024[C]

[4]	Chen C, Tramel E W, Fowler J E. Compressed-sensing recovery of images and video using multihypothesis predictions. 45th Asilomar Conference on Signals, Systems and Computers (ASILOMAR), November 6–9, 2011, Pacific Grove, USA. 2011, New York, IEEE11931198. C]

[5]	Zha Z Y, Liu X, Zhang X Get al. . Compressed sensing image reconstruction via adaptive sparse nonlocal regularization. Visual computer. 2018, 34(1): 117-137. J]

[6]	Zhang J, Zhao D B, Gao W. Group-based sparse representation for image restoration. IEEE transactions on image processing. 2014, 23(8): 3336-3351. J]

[7]	Zha Z Y, Yuan X, Wen B Het al. . From rank estimation to rank approximation: rank residual constraint for image restoration. IEEE transactions on image processing. 2020, 29: 3254-3269. J]

[8]	Zha Z Y, Wen B H, Yuan Xet al. . A hybrid structural sparsification error model for image restoration. IEEE transactions on neural networks and learning systems. 2022, 33(9): 4451-4465. J]

[9]	Zha Z Y, Wen B H, Yuan Xet al. . Low-rankness guided group sparse representation for image restoration. IEEE transactions on neural networks and learning systems. 2023, 34(10): 7593-7607. J]

[10]	Zha Z Y, Wen B H, Yuan Xet al. . Structured residual sparsity for video compressive sensing reconstruction. Signal processing. 2024, 222(2): 109513. J]

[11]	Bioucas-Dias J, Figueiredo M. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE transactions on image processing. 2007, 16122992-3004. J]

[12]	Zhang T, Zhang W, Liu Xet al. . Multifrequency magnetic induction tomography for hemorrhagic stroke detection using an adaptive threshold split Bregman algorithm. IEEE transactions on instrumentation and measurement. 2022, 71(4005713): 1-13[J]

[13]	Huang Q, Li Z, Han Yet al. . Compressed sensing based on an improved K-SVD for vibration signal compression reconstruction in wireless sensor networks. IEEE transactions on instrumentation and measurement. 2024, 73(9511311): 1-11[J]

[14]	Liu S, Ma J, Cui C. FPGA implementation of threshold projection orthogonal matching pursuit algorithm for compressed sensing reconstruction. IEEE transactions on circuits and systems I: regular papers. 2024, 71(3): 1184-1197[J]

[15]	Suo J, Wang T, Zhang Xet al. . HIT-UAV: a high-altitude infrared thermal dataset for unmanned aerial vehicle-based object detection. Scientific data. 2023, 10(1): 227. J]

[16]	Sun Y, Cao B, Zhu Pet al. . Drone-based RGB-infrared cross-modality vehicle detection via uncertainty-aware learning. IEEE transactions on circuits and systems for video technology. 2022, 32106700-6713. J]

[17]	He Y, Zhang R, Xi Cet al. . Learning background restoration and local sparse dictionary for infrared small target detection. Optoelectronics letters. 2024, 20(7): 437-448. J]

[18]	Zha Z Y, Wen B H, Yuan Xet al. . Learning nonlocal sparse and low-rank models for image compressive sensing: nonlocal sparse and low-rank modeling. IEEE signal processing magazine. 2023, 40(1): 32-44. J]

[19]	Ding K, Ma K, Wang Ket al. . Image quality assessment: unifying structure and texture similarity. IEEE transactions on pattern analysis and machine intelligence. 2022, 44(5): 2567-2581[J]