{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:03:20Z","timestamp":1771466600861,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62101529"],"award-info":[{"award-number":["62101529"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs16010016","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T07:20:31Z","timestamp":1703056831000},"page":"16","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Infrared Small Dim Target Detection Using Group Regularized Principle Component Pursuit"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2727-1391","authenticated-orcid":false,"given":"Meihui","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yuxing","family":"Wei","sequence":"additional","affiliation":[{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3922-6018","authenticated-orcid":false,"given":"Bingbing","family":"Dan","sequence":"additional","affiliation":[{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0691-4025","authenticated-orcid":false,"given":"Dongxu","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-2942","authenticated-orcid":false,"given":"Jianlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.infrared.2017.03.003","article-title":"Infrared small target enhancement based on variance difference","volume":"82","author":"Nasiri","year":"2017","journal-title":"Infrared Phys. 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