[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328009-105":59,"doc-detail-328009-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","lcpnet-latent-consistent-proximal-unfolding-network-for-infrared-small-target-detection","LCPNet - Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection","","Infrared small target detection (IRSTD) targets the identification of long-range, pixel-sparse thermal objects within complex infrared backgrounds, a core problem in remote sensing. Existing deep learning approaches learn image-to-mask mappings but often ignore physical decomposition structure between target and background. Unfolding methods embed iterative optimization into networks yet still lack sufficient coupling between optimization dynamics and image-domain updates. LCPNet addresses these gaps with latent-space low-rank unfolding, a latent consistent proximal solver, and shared optimization memory to improve robustness and reduce false alarms.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/lcpnet-latent-consistent-proximal-unfolding-network-for-infrared-small-target-detection/328009/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/lcpnet-latent-consistent-proximal-unfolding-network-for-infrared-small-target-detection/328009.png","ImageObject",300,407,{"name":92,"@type":93},"Rowan","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-13","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",16,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the main goal of IRSTD in remote sensing?","Question",{"text":113,"@type":114},"IRSTD aims to identify long-distance small targets from complex infrared backgrounds, enabling reliable perception in challenging observation scenarios.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What limitations do current feed-forward and existing unfolding methods have?",{"text":118,"@type":114},"Feed-forward designs often underuse physical decomposition structure, while existing unfolding designs mainly operate in the image domain and do not fully couple updates and memory mechanisms with the underlying optimization process.",{"name":120,"@type":111,"acceptedAnswer":121},"How does LCPNet improve infrared small target detection?",{"text":122,"@type":114},"LCPNet unfolds in latent space using a valid low-rank prior, introduces a Latent Consistent Proximal solver for stable latent updates, and adds Shared Optimization Memory to coordinate guidance across unfolding stages.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},328009,1790600870,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},1099514067415,"https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502","LCPNet: Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection  \nTianfang Zhang, Fengyi Wu, Lei Li, Chang Liu, Zhenming Peng, Huaping Zhang, Xiangyang Ji  \narXiv :2607 .04603v 1 [ cs .CV] 6 Jul 2026  \nAbstract—Infrared small target detection (IRSTD) aims to identify long distance small targets from complex infrared backgrounds, and is a fundamental task in remote sensing. Deep learning methods have improved IRSTD by learning discriminative image-to-mask mappings, but such feed-forward designs often underuse physical decomposition structure between targetsand backgrounds. Deep unfolding methods partially address this issue by embedding model-driven iterations into neural networks, yet existing designs still operate mainly in image domain and use updates and memory mechanisms that are not fully coupled with underlying optimization process. To address these limitations, we propose Latent Consistent Proximal unfolding network (LCPNet). First, we verify that low-rank prior remains valid in latent representations and perform unfolding in this space, preserving physical constraint while avoiding repeated compression of intermediate states. Second, we derive a Latent Consistent Proximal (LCP) solver that evolves each latent variable from its previous state rather than reconstructing through an indirect residual, and stabilizes small target updates through taskadaptive normalization and gain control. Third, we introduce Shared Optimization Memory (SOM), a common historical state shared by all decomposition variables to provide coordinated guidance across unfolding stages. Extensive experiments on four public benchmarks demonstrate that LCPNet outperforms stateof-the-art methods while achieving accurate and robust detection with low false alarms and competitive efficiency. Model and code are available at [https://github.com/Tianfang-Zhang/LCPNet](https://github.com/Tianfang-Zhang/LCPNet).  \nIndex Terms—Infrared small target detection, deep unfolding network, interpretability, low-rank and sparse decomposition, image segmentation.  \nI. INTRODUCTION  \nINFRARED small target detection (IRSTD) is a fundamen  \ntal capability for long-range perception systems, with broad value in remote sensing monitoring, maritime and aerial early warning, and security reconnaissance [1], [2] . Compared to other imaging techniques, infrared sensors capture thermal radiation and can operate under weak illumination, nighttime conditions, and high disturbance. This passive imaging mechanism renders IRSTD uniquely effective in scenarios where it is difficult to observe targets through conventional visual cues. Leveraging these advantages, IRSTD offers significant practi  \nCorresponding author: Xiangyang Ji.  \nTianfang Zhang, Chang Liu and Xiangyang Ji are with the Department of Automation, Tsinghua University, Beijing, 100190, China. (E-mail: sparkcar[leton@gmail.com](leton@gmail.com); {liuchang2022, [xyji](xyji}@tsinghua.edu.cn)[}](xyji}@tsinghua.edu.cn)[@tsinghua.edu.cn](xyji}@tsinghua.edu.cn))  \nFengyi Wu and Zhenming Peng are with the School of Information and Communication Engineering and the Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China, Chengdu, 610054, China. (E-mail: [wufengyi98@163.com](wufengyi98@163.com), zm[peng@uestc.edu.cn](peng@uestc.edu.cn))  \nLei Li and Huaping Zhang are with the School of Artificial Intelligence, Beijing Institute of Technology, Beijing, 100081, China. (E-mail: {lilei, [kevinzhang](kevinzhang}@bit.edu.cn)[}](kevinzhang}@bit.edu.cn)[@bit.edu.cn](kevinzhang}@bit.edu.cn))  \nIoU (%)  \n66  \n65  \n64  \n63  \n62  \n61  \n60  \n\n| L\u003Cbr>L | CPNet-6\u003Cbr>CPNet-4 |  |  |  | MS |  | HNet |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  | IR |  RP\u003Cbr>SAM | CANet+\u003Cbr> DRP | +\u003Cbr>CANet |  | |\n| ResU\u003Cbr>| Net |  | \u003Cbr> I | SNet\u003Cbr>| UIU |  | Net |\n|  |  | R | PCANet | |  |  |  |\n|  |  |  |  |  | Pa |  | rams |\n| SC | TransNet |  | AL | DNANet\u003Cbr>","cbCaidlQkGcHgXrU","https://ap.wps.com/l/cbCaidlQkGcHgXrU","pdf",21855712,18,"English","# I. INTRODUCTION\n## Challenges in infrared small target detection\n## Limitations of end-to-end deep learning approaches\n## Unfolding networks and the proposed direction","[{\"question\":\"What is the main goal of IRSTD in remote sensing?\",\"answer\":\"IRSTD aims to identify long-distance small targets from complex infrared backgrounds, enabling reliable perception in challenging observation scenarios.\"},{\"question\":\"What limitations do current feed-forward and existing unfolding methods have?\",\"answer\":\"Feed-forward designs often underuse physical decomposition structure, while existing unfolding designs mainly operate in the image domain and do not fully couple updates and memory mechanisms with the underlying optimization process.\"},{\"question\":\"How does LCPNet improve infrared small target detection?\",\"answer\":\"LCPNet unfolds in latent space using a valid low-rank prior, introduces a Latent Consistent Proximal solver for stable latent updates, and adds Shared Optimization Memory to coordinate guidance across unfolding stages.\"}]","LCPNet - Latent Consistent Proximal Unfolding Network for Infrared Small Target Detection | PDF",1789978484,45]