{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T13:44:00Z","timestamp":1768916640717,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62075219"],"award-info":[{"award-number":["62075219"]}]},{"name":"National Natural Science Foundation of China","award":["61805244"],"award-info":[{"award-number":["61805244"]}]},{"name":"National Natural Science Foundation of China","award":["20190303094SF"],"award-info":[{"award-number":["20190303094SF"]}]},{"name":"Key Technological Research Projects of Jilin Province, China","award":["62075219"],"award-info":[{"award-number":["62075219"]}]},{"name":"Key Technological Research Projects of Jilin Province, China","award":["61805244"],"award-info":[{"award-number":["61805244"]}]},{"name":"Key Technological Research Projects of Jilin Province, China","award":["20190303094SF"],"award-info":[{"award-number":["20190303094SF"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.<\/jats:p>","DOI":"10.3390\/rs14184615","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T01:35:10Z","timestamp":1663292110000},"page":"4615","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4560-1734","authenticated-orcid":false,"given":"Xiaolong","family":"Chen","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuping","family":"Tao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0820-6161","authenticated-orcid":false,"given":"Tan","family":"Gao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100039, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2090-6032","authenticated-orcid":false,"given":"Qinping","family":"Feng","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjie","family":"Piao","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"Key Laboratory of Space-Based Dynamics and Rapid Optical Imaging Technology, Chinese Academy of Sciences, Changchun 130033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","unstructured":"Dawson, J.A., and Bankston, C.T. (2010, January 14\u201317). Space debris characterization using thermal imaging systems. Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, Wailea, Maui, HI, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s10044-013-0361-7","article-title":"Analysis of small infrared target features and learning-based false detection removal for infrared search and track","volume":"17","author":"Kim","year":"2014","journal-title":"Pattern Anal. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1364\/OL.28.000531","article-title":"Automatic detection of small objects from their infrared state-of-polarization vectors","volume":"28","author":"Sadjadi","year":"2003","journal-title":"Opt. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13210","DOI":"10.3390\/s140713210","article-title":"Small infrared target detection by region-adaptive clutter rejection for sea-based infrared search and track","volume":"14","author":"Kim","year":"2014","journal-title":"Sensors"},{"key":"ref_5","first-page":"231","article-title":"Adaptive sequential algorithms for detecting targets in a heavy IR clutter","volume":"3809","author":"Tartakovsky","year":"1999","journal-title":"SPIE Proc, Signal Data Process. Small Targets (SDPST)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1109\/7.543865","article-title":"Performance of dynamic programming techniques for track-before-detect","volume":"32","author":"Tonissen","year":"1996","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Luo, J.H., Ji, H.B., and Liu, J. (2006, January 17\u201322). An algorithm based on spatial filter for infrared small target detection and its application to an all directional IRST system. Proceedings of the 27th International Congress on High-Speed Photography and Photonics, Xi\u2019an, China.","DOI":"10.1117\/12.725333"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/7.7174","article-title":"Optical moving target detection with 3-D matched filtering","volume":"24","author":"Reed","year":"2002","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1049\/iet-ipr.2017.0353","article-title":"Small target detection based on reweighted infrared patch-image model","volume":"12","author":"Guo","year":"2018","journal-title":"IET Image Process."},{"key":"ref_10","first-page":"2","article-title":"Morphology-based algorithm for point target detection in infrared backgrounds","volume":"1954","author":"Tom","year":"1993","journal-title":"Signal Data Process. Small Targets"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Deshpande, S.D., Meng, H.E., Ronda, V., and Chan, P. (1999, January 18). Max-Mean and Max-Median Filters for Detection of Small-Targets. Proceedings of the SPIE\u2019s International Symposium on Optical Science, Engineering and Instrumentation, Denver, CO, USA.","DOI":"10.1117\/12.364049"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5807","DOI":"10.1109\/JSTARS.2020.3024642","article-title":"Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection","volume":"13","author":"Xia","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2013.2242477","article-title":"A Local Contrast Method for Small Infrared Target Detection","volume":"52","author":"Chen","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2168","DOI":"10.1109\/LGRS.2014.2323236","article-title":"A Robust Infrared Small Target Detection Algorithm Based on Human Visual System","volume":"11","author":"Han","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2016.04.002","article-title":"Multiscale patch-based contrast measure for small infrared target detection","volume":"58","author":"Wei","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/JSTARS.2020.3038442","article-title":"Infrared Small Target Detection Utilizing the Enhanced Closest-Mean Background Estimation","volume":"14","author":"Han","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2452","DOI":"10.1109\/TGRS.2017.2781143","article-title":"Derivative Entropy-Based Contrast Measure for Infrared Small-Target Detection","volume":"56","author":"Bai","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4996","DOI":"10.1109\/TIP.2013.2281420","article-title":"Infrared Patch-Image Model for Small Target Detection in a Single Image","volume":"22","author":"Gao","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1109\/TPAMI.2015.2465956","article-title":"Partial sum minimization of Singular Values in Robust PCA: Algorithm and applications","volume":"38","author":"Oh","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.infrared.2017.01.009","article-title":"Non-negative infrared patch-image model: Robust target-background separation via partial sum minimization of singular values","volume":"81","author":"Dai","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Peng, Z. (2019). Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm. Remote Sens., 11.","DOI":"10.3390\/rs11040382"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.imavis.2017.04.002","article-title":"Infrared dim target detection based on total variation regularization and principal component pursuit","volume":"63","author":"Wang","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.neucom.2020.08.065","article-title":"Infrared small target detection via self-regularized weighted sparse model - ScienceDirect","volume":"420","author":"Tz","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.1109\/TGRS.2017.2709250","article-title":"Infrared dim and small target detection based on stable multisubspace learning in heterogeneous scene","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s11036-019-01377-6","article-title":"Infrared dim and small target detection based on denoising autoencoder network","volume":"25","author":"Shi","year":"2020","journal-title":"Mob. Netw. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/ipr2.12001","article-title":"CNN-based infrared dim small target detection algorithm using target-oriented shallow-deep features and effective small anchor","volume":"15","author":"Du","year":"2021","journal-title":"IET Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.jvcir.2019.05.013","article-title":"Dim and small target detection based on feature mapping neural networks","volume":"62","author":"Gao","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1007\/s11042-019-7412-z","article-title":"Dim small target detection based on convolutinal neural network in star image","volume":"79","author":"Xue","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103271","DOI":"10.1016\/j.dsp.2021.103271","article-title":"Dim and small target detection based on their living environment","volume":"120","author":"Zhou","year":"2022","journal-title":"Digit. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","article-title":"Nonlinear total variation based noise removal algorithms","volume":"60","author":"Rudin","year":"1992","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1137\/080730251","article-title":"Improved Total Variation-Type Regularization Using Higher Order Edge Detectors","volume":"3","author":"Stefan","year":"2010","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1137\/S1064827598344169","article-title":"High-order total variation-based image restoration","volume":"22","author":"Chan","year":"2001","journal-title":"SIAM J. Sci. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1137\/090769521","article-title":"Total Generalized Variation","volume":"3","author":"Bredies","year":"2010","journal-title":"SIAM J. Imaging Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, X., Chen, Y., Peng, Z., and Wu, J. (2019). Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation. Sensors, 19.","DOI":"10.3390\/s19235139"},{"key":"ref_35","unstructured":"Li, C. (2011). An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing. [Ph.D. Thesis, Rice University]."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1111\/j.1467-9868.2005.00532.x","article-title":"Model selection and estimation in regression with grouped variables","volume":"68","author":"Yuan","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1137\/070698920","article-title":"Fixed-point continuation for l1-minimization: Methodology and convergence","volume":"19","author":"Hale","year":"2008","journal-title":"SIAM J. Optim."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.patcog.2017.11.016","article-title":"Infrared small-dim target detection based on Markov random field guided noise modeling","volume":"76","author":"Gao","year":"2018","journal-title":"Pattern Recognit."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4615\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:32:19Z","timestamp":1760142739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4615"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,15]]},"references-count":38,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184615"],"URL":"https:\/\/doi.org\/10.3390\/rs14184615","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,15]]}}}