Infrared Small Target Detection with Total Variation and Reweighted <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> Regularization

Fang, Houzhang; Chen, Min; Liu, Xiyang; Yao, Shoukui

doi:10.1155/2020/1529704

Cited by 7 publications

(6 citation statements)

References 36 publications

(78 reference statements)

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“…Thereby, the background matrix can be considered as a low-rank component, while brighter small targets occupy little pixels, which can be considered sparse. In this way, the target detection problem is transformed into a robust principal component analysis (RPCA) problem, which has been widely used since it was proposed [46,47].…”

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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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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“…Many researchers [57][58][59] have employed reweighting methods to speed up the convergence and reduce the iteration calculation. We adopted the following reweighting method to speed up the proposed algorithm iteration process,…”

Section: The Proposed Enhanced Ipt Modelmentioning

confidence: 99%

An Enhanced Image Patch Tensor Decomposition for Infrared Small Target Detection

Huang

Song

et al. 2022

Remote Sensing

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Infrared small-target detection is a key technology for the infrared search and track system (IRST), but some problems still exist, such as false detections in complex backgrounds and clutter. To solve these problems, a novel image patch tensor (IPT) model for infrared small-target detection is proposed. First, to better estimate the background component, we utilize the Laplace operator to approximate the background tensor rank. Secondly, we combined local gradient features and highlighted area indicators to model the local targets prior, which can effectively suppress the complex background clutter. The proposed model was solved by the alternating direction method of multipliers (ADMM). The experimental results on various scenes show that our model achieves an excellent performance in suppressing strong edge clutter and estimating small targets.

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“…Total variation (TV) regularization is widely used in infrared small target detection, such as [31], [45], [46] because of its good performance in preserving the spatial piecewise smoothness, edge structure and spatial sparsity of the images. However, existing methods are based on matrix framework and can only describe the spatial continuity of small targets, but ignore their temporal continuity.…”

Section: B Asymmetric Spatial-temporal Total Variation Regularizationmentioning

confidence: 99%

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

Liu,

Yang,

et al. 2021

Preprint

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Infrared small target detection plays an important role in many infrared systems. Recently, many infrared small target detection methods have been proposed, in which the lowrank model has been used as a powerful tool. However, most low-rank-based methods assign the same weights for different singular values, which will lead to inaccurate background estimation. Considering that different singular values have different importance and should be treated discriminatively, in this paper, we propose a non-convex tensor low-rank approximation (NTLA) method for infrared small target detection. In our method, NTLA adaptively assigns different weights to different singular values for accurate background estimation. Based on the proposed NTLA, we use the asymmetric spatial-temporal total variation (ASTTV) to thoroughly describe background feature, which can achieve good background estimation and detection in complex scenes. Compared with the traditional total variation approach, ASTTV exploits different smoothness strength for spatial and temporal regularization. We develop an efficient algorithm to find the optimal solution of the proposed model. Compared with some state-of-the-art methods, the proposed method achieve an improvement in different evaluation metrics. Extensive experiments on both synthetic and real data demonstrate the proposed method provide a more robust detection in complex situations with low false rates.

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Infrared Small Target Detection with Total Variation and Reweighted ℓ1 Regularization

Cited by 7 publications

References 36 publications

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

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

An Enhanced Image Patch Tensor Decomposition for Infrared Small Target Detection

Non-Convex Tensor Low-Rank Approximation for Infrared Small Target Detection

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