Multiframe Infrared Small Target Detection via Novel Low-Rank Approximation and Robust CUR Decomposition.
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| Title: | Multiframe Infrared Small Target Detection via Novel Low-Rank Approximation and Robust CUR Decomposition. |
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| Authors: | Zhu, Hui1 (AUTHOR), Feng, Xiangchu1 (AUTHOR) xcfeng@mail.xidian.edu.cn |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p892. 17p. |
| Subjects: | Low-rank matrices, Matrix decomposition, Mathematical optimization, Infrared photography, Signal detection |
| Abstract: | Highlights: What are the main findings? We refine the existing low-rank matrix decomposition approaches, allowing the low-rank component of multi-frame infrared images to be more accurately approximated via an abundance matrix. Traditional multi-frame infrared small-target detection methods based on low-rank and sparse decomposition suffer from high computational complexity and extremely time-consuming optimization. By introducing column-row (CUR) decomposition into the iterative optimization process, the proposed method significantly reduces the overall computational cost of the algorithm. What are the implications of the main findings? The proposed low-rank approximation enables more accurate recovery of background components in multi-frame infrared images, effectively suppressing background clutter and reducing false detections. As a result, it improves the detection accuracy of low-rank sparse-based infrared small-target detection methods. The effective integration of the improved low-rank approximation with robust CUR decomposition enables the proposed method to rapidly and accurately detect infrared small targets in multi-frame infrared imagery. Low-rank sparse decomposition models have become the mainstream optimization framework for multiframe infrared small target detection. Existing low-rank matrix decomposition approximations typically pre-decompose infrared videos into the product of two low-rank matrices to capture the background's low-rank characteristics. However, such approximations are not optimal and often result in suboptimal background recovery. To achieve more accurate low-rank recovery, we exploit the intrinsic relationship between low-rank matrices and their generalized inverse matrices, thereby improving conventional decomposition approximations. Moreover, to address the high computational cost of applying low-rank and sparse decomposition models to multi-frame infrared videos, we introduce a robust column-row (CUR) decomposition to accelerate the iterative process, thereby significantly improving computational efficiency. The experimental results show that the proposed method achieves fast detection of small targets in infrared videos while maintaining competitive detection performance. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We refine the existing low-rank matrix decomposition approaches, allowing the low-rank component of multi-frame infrared images to be more accurately approximated via an abundance matrix. Traditional multi-frame infrared small-target detection methods based on low-rank and sparse decomposition suffer from high computational complexity and extremely time-consuming optimization. By introducing column-row (CUR) decomposition into the iterative optimization process, the proposed method significantly reduces the overall computational cost of the algorithm. What are the implications of the main findings? The proposed low-rank approximation enables more accurate recovery of background components in multi-frame infrared images, effectively suppressing background clutter and reducing false detections. As a result, it improves the detection accuracy of low-rank sparse-based infrared small-target detection methods. The effective integration of the improved low-rank approximation with robust CUR decomposition enables the proposed method to rapidly and accurately detect infrared small targets in multi-frame infrared imagery. Low-rank sparse decomposition models have become the mainstream optimization framework for multiframe infrared small target detection. Existing low-rank matrix decomposition approximations typically pre-decompose infrared videos into the product of two low-rank matrices to capture the background's low-rank characteristics. However, such approximations are not optimal and often result in suboptimal background recovery. To achieve more accurate low-rank recovery, we exploit the intrinsic relationship between low-rank matrices and their generalized inverse matrices, thereby improving conventional decomposition approximations. Moreover, to address the high computational cost of applying low-rank and sparse decomposition models to multi-frame infrared videos, we introduce a robust column-row (CUR) decomposition to accelerate the iterative process, thereby significantly improving computational efficiency. The experimental results show that the proposed method achieves fast detection of small targets in infrared videos while maintaining competitive detection performance. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18060892 |
