scholarly journals Infrared Small Target Detection Based on Non-Overlapping Patch Model via l0-l1 Norm

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1426
Author(s):  
Jiaqi Yang ◽  
Yi Cui ◽  
Fei Song ◽  
Tao Lei
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Optimization Method ◽  
Low Rank ◽  
Computational Time ◽  
Background Suppression ◽  
Small Target ◽  
L1 Norm ◽  
Small Target Detection ◽  
Infrared Small Target

Infrared small target detection technology has sufficient applications in many engineering fields, such as infrared early warning, infrared tracking, and infrared reconnaissance. Due to the tiny size of the infrared small target and the lack of shape and texture information, existing methods often leave residuals or miss the target. To address these issues, a novel method based on a non-overlapping patch (NOP) joint l0-l1 norm is proposed with the introduction of sparsity regularized principal component pursuit (SRPCP). The NOP model makes the patch lighter in the first place, reducing time consumption. The adoption of the l0 norm enhances the sparsity of the target, while the adoption of the l1 norm enhances the robustness of the algorithm under clutter. As a smart optimization method, SRPCP solves the NOP model fittingly and achieves stable separation of low-rank and sparse components, thereby improving detection capacity while suppressing the background efficiently. The proposed method ultimately yielded favorable detection results. Adequate experiment results demonstrate that the proposed method is competitive in terms of background suppression and true target detection with respect to state-of-the-art methods. In addition, our method also reduces the computational time.

Infrared Small Target Detection Based on Morphology and SUSAN Algorithm

2019 ◽  
Vol 34 (06) ◽  
pp. 2050013 ◽  
Author(s):  
Zhiwei Hu ◽  
Yixin Su
Keyword(s):  
Target Detection ◽  
Infrared Image ◽  
Frequency Region ◽  
Guidance System ◽  
Background Suppression ◽  
Small Target ◽  
High Frequency Region ◽  
Small Target Detection ◽  
Small Targets ◽  
Infrared Small Target

Infrared small target detection is one of the key techniques in infrared imaging guidance system. The technology of infrared small target detection still needs to be further studied to improve the detection performance. This paper combines the high-pass filtering characteristics of morphological top-hat transform with SUSAN algorithm, and proposes a small infrared target detection method based on morphology and SUSAN algorithm. This method uses top-hat transform to detect the high-frequency region in infrared image, and filters out the low-frequency region in the image to implement the preliminary background suppression of infrared image. Then the SUSAN algorithm is used to detect small targets in the image after background suppression. The proposed method is applied to the single infrared image which is acquired by the infrared guidance system in the process of detecting and tracking the target under specific conditions. The experimental results show that the method is effective and can detect infrared small targets under different background.

Small Target Detection for Infrared Image Based on Optimal Infrared Patch-Image Model by Solving Modified Adaptive RPCA Problem

2020 ◽  
pp. 2150007
Author(s):  
Bin Xiong ◽  
Xinhan Huang ◽  
Min Wang ◽  
Gang Peng
Keyword(s):  
Target Detection ◽  
Principal Component ◽  
Low Rank ◽  
Small Target ◽  
Image Model ◽  
Threshold Method ◽  
Small Target Detection ◽  
Augmented Lagrange Multiplier ◽  
Small Targets ◽  
Patch Image

Small target detection in infrared (IR) images has been widely applied for both military and civilian purposes. In this study, because IR images contain sparse and low-rank features in most scenarios, we propose an optimal IR patch-image (OIPI) model-based detection method to detect small targets in heavily cluttered IR images. First, the OIPI model was generated based on a conventional IR image model using a novel optimal patch size and sliding step adaptive selection algorithm. Secondly, the sparse and low-rank features of IR images were extracted and fused to generate an adaptive weighted parameter. Thirdly, the adaptive inexact augmented Lagrange multiplier (AIALM) algorithm was applied in the OIPI model to solve the robust principal component analysis (RPCA) optimization problem. Finally, an adaptive threshold method is proposed to segment and calibrate targets. Experimental results indicate that the proposed algorithm is capable of detecting small targets more stably and accurately, compared with state-of-the-art methods.

scholarly journals Infrared Small Target Detection Based on Non-Convex Optimization with Lp-Norm Constraint

2019 ◽  
Vol 11 (5) ◽  
pp. 559 ◽  
Author(s):  
Tianfang Zhang ◽  
Hao Wu ◽  
Yuhan Liu ◽  
Lingbing Peng ◽  
Chunping Yang ◽  
...  
Keyword(s):  
Convex Optimization ◽  
Target Detection ◽  
Detection Efficiency ◽  
Infrared Image ◽  
Low Rank ◽  
Small Target ◽  
Small Target Detection ◽  
Lp Norm ◽  
Norm Constraint ◽  
Infrared Small Target

The infrared search and track (IRST) system has been widely used, and the field of infrared small target detection has also received much attention. Based on this background, this paper proposes a novel infrared small target detection method based on non-convex optimization with Lp-norm constraint (NOLC). The NOLC method strengthens the sparse item constraint with Lp-norm while appropriately scaling the constraints on low-rank item, so the NP-hard problem is transformed into a non-convex optimization problem. First, the infrared image is converted into a patch image and is secondly solved by the alternating direction method of multipliers (ADMM). In this paper, an efficient solver is given by improving the convergence strategy. The experiment shows that NOLC can accurately detect the target and greatly suppress the background, and the advantages of the NOLC method in detection efficiency and computational efficiency are verified.

scholarly journals A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 567 ◽  
Author(s):  
Wen-Huan Cao ◽  
Shu-Cai Huang
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Adaptive Threshold ◽  
Computational Time ◽  
Gram Matrix ◽  
Small Target ◽  
Two Dimensional ◽  
Threshold Method ◽  
Small Target Detection ◽  
Infrared Small Target

By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average.

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