{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T09:04:47Z","timestamp":1770541487169,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T00:00:00Z","timestamp":1700438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Light of West China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time.<\/jats:p>","DOI":"10.3390\/rs15225424","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T01:54:12Z","timestamp":1700445252000},"page":"5424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Infrared Small-Target Detection Based on Background-Suppression Proximal Gradient and GPU Acceleration"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4957-6364","authenticated-orcid":false,"given":"Xuying","family":"Hao","sequence":"first","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3084-519X","authenticated-orcid":false,"given":"Xianyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5105-6045","authenticated-orcid":false,"given":"Yujia","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Yi","family":"Cui","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-1582","authenticated-orcid":false,"given":"Tao","family":"Lei","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,20]]},"reference":[{"key":"ref_1","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_2","first-page":"5001314","article-title":"Infrared Small Target Detection via Interpatch Correlation Enhancement and Joint Local Visual Saliency Prior","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.patcog.2009.12.023","article-title":"Analysis of new top-hat transformation and the application for infrared dim small target detection","volume":"43","author":"Bai","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.infrared.2014.03.006","article-title":"Bilateral two-dimensional least mean square filter for infrared small target detection","volume":"65","author":"Zhao","year":"2014","journal-title":"Infrared Phys. Technol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Deshpande, S.D., Er, M.H., Venkateswarlu, R., and Chan, P. (1999, January 20\u201322). Max-mean and max-median filters for detection of small targets. Proceedings of the Signal and Data Processing of Small Targets, Denver, CO, USA.","DOI":"10.1117\/12.364049"},{"key":"ref_6","first-page":"7507005","article-title":"Moving dim and small target detection in multiframe infrared sequence with low SCR based on temporal profile similarity","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1890","DOI":"10.1109\/LGRS.2016.2616416","article-title":"Effective infrared small target detection utilizing a novel local contrast method","volume":"13","author":"Qin","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1109\/LGRS.2018.2790909","article-title":"Infrared small target detection utilizing the multiscale relative local contrast measure","volume":"15","author":"Han","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","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_10","first-page":"7000605","article-title":"Small infrared target detection based on fast adaptive masking and scaling with iterative segmentation","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","first-page":"7505705","article-title":"Infrared small target detection based on weighted three-layer window local contrast","volume":"19","author":"Cui","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","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_13","first-page":"3000412","article-title":"A spatial-temporal feature-based detection framework for infrared dim small target","volume":"60","author":"Du","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104283","DOI":"10.1016\/j.infrared.2022.104283","article-title":"Infrared target detection in marine images with heavy waves via local patch similarity","volume":"125","author":"Zhang","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TIP.2022.3199107","article-title":"Dense nested attention network for infrared small target detection","volume":"32","author":"Li","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112662","DOI":"10.1016\/j.measurement.2023.112662","article-title":"Infrared small target detection based on local-image construction and maximum correntropy","volume":"211","author":"Zhong","year":"2023","journal-title":"Measurement"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.infrared.2016.06.021","article-title":"Infrared small target and background separation via column-wise weighted robust principal component analysis","volume":"77","author":"Dai","year":"2016","journal-title":"Infrared Phys. Technol."},{"key":"ref_19","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_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","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_22","doi-asserted-by":"crossref","unstructured":"Zhang, L., Peng, L., Zhang, T., Cao, S., and Peng, Z. (2018). Infrared small target detection via non-convex rank approximation minimization joint l 2, 1 norm. Remote Sens., 10.","DOI":"10.3390\/rs10111821"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, T., Wu, H., Liu, Y., Peng, L., Yang, C., and Peng, Z. (2019). Infrared small target detection based on non-convex optimization with Lp-norm constraint. Remote Sens., 11.","DOI":"10.3390\/rs11050559"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, W., Tao, S., Gao, T., Feng, Q., and Piao, Y. (2022). Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection. Remote Sens., 14.","DOI":"10.3390\/rs14184615"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"104222","DOI":"10.1016\/j.infrared.2022.104222","article-title":"Infrared small target detection using kernel low-rank approximation and regularization terms for constraints","volume":"125","author":"Yan","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_26","first-page":"5614317","article-title":"Single-Frame Infrared Small Target Detection by High Local Variance, Low-Rank and Sparse Decomposition","volume":"61","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.patcog.2016.07.036","article-title":"Entropy-based window selection for detecting dim and small infrared targets","volume":"61","author":"Deng","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_28","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_29","first-page":"5000816","article-title":"Infrared Small Target Detection Based on Local Contrast-Weighted Multidirectional Derivative","volume":"61","author":"Xu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.infrared.2017.12.018","article-title":"Infrared small target detection based on local intensity and gradient properties","volume":"89","author":"Zhang","year":"2018","journal-title":"Infrared Phys. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7687","DOI":"10.1109\/JSTARS.2022.3204315","article-title":"Infrared Small Target Detection Based on Gradient-Intensity Joint Saliency Measure","volume":"15","author":"Li","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9813","DOI":"10.1109\/TGRS.2020.3044958","article-title":"Attentional local contrast networks for infrared small target detection","volume":"59","author":"Dai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","unstructured":"Wang, H., Zhou, L., and Wang, L. (November, January 27). Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_34","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","volume":"420","author":"Zhang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.infrared.2013.06.002","article-title":"Separable convolution template (SCT) background prediction accelerated by CUDA for infrared small target detection","volume":"60","author":"Wu","year":"2013","journal-title":"Infrared Phys. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3752","DOI":"10.1109\/JSTARS.2017.2700023","article-title":"Reweighted infrared patch-tensor model with both nonlocal and local priors for single-frame small target detection","volume":"10","author":"Dai","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104192","DOI":"10.1016\/j.infrared.2022.104192","article-title":"Robust and fast infrared small target detection based on pareto frontier optimization","volume":"123","author":"Xu","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_38","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_39","first-page":"5000321","article-title":"Infrared small target detection via nonconvex tensor fibered rank approximation","volume":"60","author":"Kong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","first-page":"5001417","article-title":"Infrared Small Target Detection Using Nonoverlapping Patch Spatial\u2014Temporal Tensor Factorization With Capped Nuclear Norm Regularization","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"5000417","article-title":"Sparse Regularization-Based Spatial-Temporal Twist Tensor Model for Infrared Small Target Detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Ding, C., Gao, Z., and Xie, C. (2023). ANLPT: Self-Adaptive and Non-Local Patch-Tensor Model for Infrared Small Target Detection. Remote Sens., 15.","DOI":"10.3390\/rs15041021"},{"key":"ref_43","first-page":"15","article-title":"An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems","volume":"6","author":"Toh","year":"2010","journal-title":"Pac. J. Optim."},{"key":"ref_44","unstructured":"Lin, Z., Ganesh, A., Wright, J., Wu, L., Chen, M., and Ma, Y. (2023, September 30). Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix. Available online: https:\/\/people.eecs.berkeley.edu\/~yima\/matrix-rank\/Files\/rpca_algorithms.pdf."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"9478","DOI":"10.1007\/s11227-020-03214-0","article-title":"GPU-accelerated registration of hyperspectral images using KAZE features","volume":"76","author":"Heras","year":"2020","journal-title":"J. Supercomput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s11554-021-01187-8","article-title":"Real-time optical flow processing on embedded GPU: An hardware-aware algorithm to implementation strategy","volume":"19","author":"Seznec","year":"2022","journal-title":"J. Real Time Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5424\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:25:59Z","timestamp":1760131559000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/22\/5424"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,20]]},"references-count":46,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15225424"],"URL":"https:\/\/doi.org\/10.3390\/rs15225424","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,20]]}}}