Infrared Small Target Detection via Two-Stage Feature Complementary Improved Tensor Low-Rank Sparse Decomposition Article Swipe
Zixu Huang
,
Erwei Zhao
,
Wei Zheng
,
Xiaodong Peng
,
Wenlong Niu
,
Zhen Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3463017
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2024.3463017
Infrared small target detection has been widely used in military and civil fields. However, due to the insufficient feature integration capabilities of existing methods, effectively separating strong background clutter and targets in complex scenes remains difficult. To address this issue, we propose a two-stage feature complementary improved tensor low-rank sparse decomposition (TLRSD) method. The detection process is divided into two stages: tensor initialization and tensor decomposition, effectively integrating local and nonlocal features. In the tensor initialization stage, inspired by the local saliency of the target and the local consistency of the background, we design a three-layer directional filtering (TLDF) operator for preliminary clutter suppression and target enhancement. Then, to promote the complementary advantages of local and nonlocal features, we refer to the TLDF and the original image to provide a targeted initialization strategy for the TLRSD model. In the tensor decomposition stage, we develop a robust partial sum of the tubal nuclear norm as a nonconvex approximation of tensor rank, which can adaptively adjust the singular value distribution, thus adapting to diversity scenes. Meanwhile, we finely adjust the balance between low-rank and sparse components in the model-solving process through a nonlinear reweighting strategy, accelerating the optimization convergence speed and improving the model's background recovery ability. Extensive experiments on five practical datasets demonstrate that the proposed method is more effective and robust compared to ten state-of-the-art approaches.
Related Topics
Concepts
Pattern recognition (psychology)
Artificial intelligence
Computer science
Decomposition
Rank (graph theory)
Feature (linguistics)
Feature extraction
Infrared
Matrix decomposition
Tensor (intrinsic definition)
Tensor decomposition
Stage (stratigraphy)
Mathematics
Physics
Chemistry
Optics
Geology
Combinatorics
Pure mathematics
Philosophy
Eigenvalues and eigenvectors
Quantum mechanics
Organic chemistry
Paleontology
Linguistics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2024.3463017
- OA Status
- gold
- Cited By
- 5
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402592852
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402592852Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/jstars.2024.3463017Digital Object Identifier
- Title
-
Infrared Small Target Detection via Two-Stage Feature Complementary Improved Tensor Low-Rank Sparse DecompositionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Zixu Huang, Erwei Zhao, Wei Zheng, Xiaodong Peng, Wenlong Niu, Zhen YangList of authors in order
- Landing page
-
https://doi.org/10.1109/jstars.2024.3463017Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1109/jstars.2024.3463017Direct OA link when available
- Concepts
-
Pattern recognition (psychology), Artificial intelligence, Computer science, Decomposition, Rank (graph theory), Feature (linguistics), Feature extraction, Infrared, Matrix decomposition, Tensor (intrinsic definition), Tensor decomposition, Stage (stratigraphy), Mathematics, Physics, Chemistry, Optics, Geology, Combinatorics, Pure mathematics, Philosophy, Eigenvalues and eigenvectors, Quantum mechanics, Organic chemistry, Paleontology, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5Per-year citation counts (last 5 years)
- References (count)
-
67Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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