Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery
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Abstract:
In recent years, some model-based block compressive sensing (BCS) algorithms utilize the nonlocal self-similarity prior to obtain good restoration performance from the statistical characteristics of the entire natural image. However, for low-subrate infrared aerial images rather than natural images, these nonlocal-prior reconstruction algorithms are usually less effective than local-prior reconstruction algorithms. Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate 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, which adopts both split Bregman iteration 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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Project Supported:
The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)
Clc Number:
