Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy
Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-ten...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/9/1520 |
| _version_ | 1797568394377035776 |
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| author | Xuewei Guan Landan Zhang Suqi Huang Zhenming Peng |
| author_facet | Xuewei Guan Landan Zhang Suqi Huang Zhenming Peng |
| author_sort | Xuewei Guan |
| collection | DOAJ |
| description | Small target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="script">l</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> </inline-formula>-norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="script">l</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>-norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods. |
| first_indexed | 2024-03-10T19:55:24Z |
| format | Article |
| id | doaj.art-4d3b53df723748a298c72bf8209518ed |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:55:24Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4d3b53df723748a298c72bf8209518ed2023-11-19T23:57:32ZengMDPI AGRemote Sensing2072-42922020-05-01129152010.3390/rs12091520Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast EnergyXuewei Guan0Landan Zhang1Suqi Huang2Zhenming Peng3School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, ChinaSmall target detection is a crucial technique that restricts the performance of many infrared imaging systems. In this paper, a novel detection model of infrared small target via non-convex tensor rank surrogate joint local contrast energy (NTRS) is proposed. To improve the latest infrared patch-tensor (IPT) model, a non-convex tensor rank surrogate merging tensor nuclear norm (TNN) and the Laplace function, is utilized for low rank background patch-tensor constraint, which has a useful property of adaptively allocating weight for every singular value and can better approximate <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="script">l</mi> <mn>0</mn> </msub> </mrow> </semantics> </math> </inline-formula>-norm. Considering that the local prior map can be equivalent to the saliency map, we introduce a local contrast energy feature into IPT detection framework to weight target tensor, which can efficiently suppress the background and preserve the target simultaneously. Besides, to remove the structured edges more thoroughly, we suggest an additional structured sparse regularization term using the <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi mathvariant="script">l</mi> <mrow> <mn>1</mn> <mo>,</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>-norm of third-order tensor. To solve the proposed model, a high-efficiency optimization way based on alternating direction method of multipliers with the fast computing of tensor singular value decomposition is designed. Finally, an adaptive threshold is utilized to extract real targets of the reconstructed target image. A series of experimental results show that the proposed method has robust detection performance and outperforms the other advanced methods.https://www.mdpi.com/2072-4292/12/9/1520infrared imagesmall target detectionnon-convex surrogatesingular value decomposition |
| spellingShingle | Xuewei Guan Landan Zhang Suqi Huang Zhenming Peng Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy Remote Sensing infrared image small target detection non-convex surrogate singular value decomposition |
| title | Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy |
| title_full | Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy |
| title_fullStr | Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy |
| title_full_unstemmed | Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy |
| title_short | Infrared Small Target Detection via Non-Convex Tensor Rank Surrogate Joint Local Contrast Energy |
| title_sort | infrared small target detection via non convex tensor rank surrogate joint local contrast energy |
| topic | infrared image small target detection non-convex surrogate singular value decomposition |
| url | https://www.mdpi.com/2072-4292/12/9/1520 |
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