Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection
The efficient and accurate detection of infrared small targets under various heterogeneous backgrounds has always been a key issue that needs to be addressed. To address this issue, this study presents a fast and robust low rank and sparse decomposition algorithm for infrared small target detection....
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10806708/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841533391572303872 |
|---|---|
| author | Yan-Shan Zhang Ze-Yin Li Dong-Dong Pang Lu-Yao Wang Cheng-Jun Wu Kang Duan Jun-Ming Gao |
| author_facet | Yan-Shan Zhang Ze-Yin Li Dong-Dong Pang Lu-Yao Wang Cheng-Jun Wu Kang Duan Jun-Ming Gao |
| author_sort | Yan-Shan Zhang |
| collection | DOAJ |
| description | The efficient and accurate detection of infrared small targets under various heterogeneous backgrounds has always been a key issue that needs to be addressed. To address this issue, this study presents a fast and robust low rank and sparse decomposition algorithm for infrared small target detection. Firstly, an infrared image patch model is constructed based on the local autocorrelation characteristics of the image, where the original infrared image can be transformed into a new infrared image through matrix decomposition and vector reconstruction. Leveraging the support of the low-rank approximation theory, the small target detection is formulated as an optimization problem involving a low-rank matrix and a sparse matrix. Specifically, in order to improve optimization efficiency, greedy bilateral smoothing is employed to model low rank backgrounds, resulting in a significant improvement in the efficiency of the detection algorithm. Then, a optimization algorithm framework based on alternating projection is designed to achieve an accurate separation of target and background. Finally, the adaptive threshold segmentation operation is adopted to extract the target. The experimental results on eight real infrared sequences demonstrate that the greedy bilateral smoothing exhibits powerful background suppression capability and can achieve higher signal-to-noise ratio gain, while maintaining stable detection performance, even in the presence of different additional noise interferences. |
| format | Article |
| id | doaj-art-27a3c351804e445e8f724416302aece5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-27a3c351804e445e8f724416302aece52025-01-16T00:01:18ZengIEEEIEEE Access2169-35362024-01-011219674019675510.1109/ACCESS.2024.351970310806708Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target DetectionYan-Shan Zhang0Ze-Yin Li1Dong-Dong Pang2https://orcid.org/0000-0002-2934-1840Lu-Yao Wang3Cheng-Jun Wu4Kang Duan5Jun-Ming Gao6School of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing, Beijing, ChinaSchool of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaSchool of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou, Henan, ChinaThe efficient and accurate detection of infrared small targets under various heterogeneous backgrounds has always been a key issue that needs to be addressed. To address this issue, this study presents a fast and robust low rank and sparse decomposition algorithm for infrared small target detection. Firstly, an infrared image patch model is constructed based on the local autocorrelation characteristics of the image, where the original infrared image can be transformed into a new infrared image through matrix decomposition and vector reconstruction. Leveraging the support of the low-rank approximation theory, the small target detection is formulated as an optimization problem involving a low-rank matrix and a sparse matrix. Specifically, in order to improve optimization efficiency, greedy bilateral smoothing is employed to model low rank backgrounds, resulting in a significant improvement in the efficiency of the detection algorithm. Then, a optimization algorithm framework based on alternating projection is designed to achieve an accurate separation of target and background. Finally, the adaptive threshold segmentation operation is adopted to extract the target. The experimental results on eight real infrared sequences demonstrate that the greedy bilateral smoothing exhibits powerful background suppression capability and can achieve higher signal-to-noise ratio gain, while maintaining stable detection performance, even in the presence of different additional noise interferences.https://ieeexplore.ieee.org/document/10806708/Infrared small target detectionlow-rank structure approximationalternating projectiongreedy bilateral smoothing |
| spellingShingle | Yan-Shan Zhang Ze-Yin Li Dong-Dong Pang Lu-Yao Wang Cheng-Jun Wu Kang Duan Jun-Ming Gao Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection IEEE Access Infrared small target detection low-rank structure approximation alternating projection greedy bilateral smoothing |
| title | Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection |
| title_full | Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection |
| title_fullStr | Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection |
| title_full_unstemmed | Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection |
| title_short | Fast Low Rank and Sparse Decomposition Based on Greedy Bilateral Smoothing for Infrared Small Target Detection |
| title_sort | fast low rank and sparse decomposition based on greedy bilateral smoothing for infrared small target detection |
| topic | Infrared small target detection low-rank structure approximation alternating projection greedy bilateral smoothing |
| url | https://ieeexplore.ieee.org/document/10806708/ |
| work_keys_str_mv | AT yanshanzhang fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT zeyinli fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT dongdongpang fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT luyaowang fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT chengjunwu fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT kangduan fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection AT junminggao fastlowrankandsparsedecompositionbasedongreedybilateralsmoothingforinfraredsmalltargetdetection |