Infrared small target detection via incorporating spatial structural prior into intrinsic tensor sparsity regularization

Zhou, Fei; Wu, Yiquan; Dai, Yawen

doi:10.1016/j.dsp.2021.102966

Cited by 18 publications

(1 citation statement)

References 38 publications

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“…where λ is a trade-off factor. Solving the L 0 norm is an NP-hard problem, and the L 1 norm is generally used instead [49].…”

Section: Image Patch Tensor (Ipt) Modelmentioning

confidence: 99%

Infrared Dim and Small Target Detection from Complex Scenes via Multi-Frame Spatial–Temporal Patch-Tensor Model

Pan

et al. 2022

Remote Sensing

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Infrared imaging plays an important role in space-based early warning and anti-missile guidance due to its particular imaging mechanism. However, the signal-to-noise ratio of the infrared image is usually low and the target is moving, which makes most of the existing methods perform inferiorly, especially in very complex scenes. To solve these difficulties, this paper proposes a novel multi-frame spatial–temporal patch-tensor (MFSTPT) model for infrared dim and small target detection from complex scenes. First, the method of simultaneous sampling in spatial and temporal domains is adopted to make full use of the information between multi-frame images, establishing an image-patch tensor model that makes the complex background more in line with the low-rank assumption. Secondly, we propose utilizing the Laplace method to approximate the rank of the tensor, which is more accurate. Third, to suppress strong interference and sparse noise, a prior weighted saliency map is established through a weighted local structure tensor, and different weights are assigned to the target and background. Using an alternating direction method of multipliers (ADMM) to solve the model, we can accurately separate the background and target components and acquire the detection results. Through qualitative and quantitative analysis, experimental results of multiple real sequences verify the rationality and effectiveness of the proposed algorithm.

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“…where λ is a trade-off factor. Solving the L 0 norm is an NP-hard problem, and the L 1 norm is generally used instead [49].…”

Section: Image Patch Tensor (Ipt) Modelmentioning

confidence: 99%