Local contrast measure with iterative error for infrared small target detection
Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixel‐sized noises with high brightness (PNHB). Based on the analysis of the inhe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-ipr.2020.1157 |
| _version_ | 1828153470659592192 |
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| author | Zujing Yan Yunhong Xin Yixuan Zhang |
| author_facet | Zujing Yan Yunhong Xin Yixuan Zhang |
| author_sort | Zujing Yan |
| collection | DOAJ |
| description | Local contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixel‐sized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two‐dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the sub‐block statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub‐block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds. |
| first_indexed | 2024-04-11T22:26:33Z |
| format | Article |
| id | doaj.art-64a03fd1c2874747bf2514d6e535b7c0 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T22:26:33Z |
| publishDate | 2020-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-64a03fd1c2874747bf2514d6e535b7c02022-12-22T03:59:39ZengWileyIET Image Processing1751-96591751-96672020-12-0114153725373210.1049/iet-ipr.2020.1157Local contrast measure with iterative error for infrared small target detectionZujing Yan0Yunhong Xin1Yixuan Zhang2School of Physics and Information TechnologyShaanxi Normal UniversityXi'anPeople's Republic of ChinaSchool of Physics and Information TechnologyShaanxi Normal UniversityXi'anPeople's Republic of ChinaSchool of Physics and Information TechnologyShaanxi Normal UniversityXi'anPeople's Republic of ChinaLocal contrast measure (LCM) has been proved to be an effective method for infrared small target detection. However, the detection performance of LCM decreases dramatically when the background contains strong edges and pixel‐sized noises with high brightness (PNHB). Based on the analysis of the inherent causes of the poor performance of LCM in extremely complex backgrounds, this study presents an effective LCM with an iterative error. The contribution is as follows: first, the two‐dimensional least mean square (TDLMS) filter with an adaptive parameter is applied to suppress the background clutters roughly in each multiscale window. Then, the partial maximum pixel mean is applied to the LCM to optimise the sub‐block statistical parameters, which achieves excellent strong edges suppression performance. Finally, the iteration error generated by TDLMS and the sub‐block weight matrix is updated alternately to further optimise the statistical parameters of the contrast measure to make it more effective in suppressing PNHB. Experimental results demonstrate that the proposed approach is not only superior to the contrast methods in terms of high detection efficiency and low false alarm rate but also has satisfactory adaptability under extremely complex backgrounds.https://doi.org/10.1049/iet-ipr.2020.1157adaptive parameterbackground clutterspartial maximum pixel meansub‐block statistical parametersiteration errorTDLMS |
| spellingShingle | Zujing Yan Yunhong Xin Yixuan Zhang Local contrast measure with iterative error for infrared small target detection IET Image Processing adaptive parameter background clutters partial maximum pixel mean sub‐block statistical parameters iteration error TDLMS |
| title | Local contrast measure with iterative error for infrared small target detection |
| title_full | Local contrast measure with iterative error for infrared small target detection |
| title_fullStr | Local contrast measure with iterative error for infrared small target detection |
| title_full_unstemmed | Local contrast measure with iterative error for infrared small target detection |
| title_short | Local contrast measure with iterative error for infrared small target detection |
| title_sort | local contrast measure with iterative error for infrared small target detection |
| topic | adaptive parameter background clutters partial maximum pixel mean sub‐block statistical parameters iteration error TDLMS |
| url | https://doi.org/10.1049/iet-ipr.2020.1157 |
| work_keys_str_mv | AT zujingyan localcontrastmeasurewithiterativeerrorforinfraredsmalltargetdetection AT yunhongxin localcontrastmeasurewithiterativeerrorforinfraredsmalltargetdetection AT yixuanzhang localcontrastmeasurewithiterativeerrorforinfraredsmalltargetdetection |