发表机构
Department of Automation, Tsinghua University; Laboratory of Imaging Detection and Intelligent Perception, University of Electronic Science and Technology of China; School of Artificial Intelligence, Beijing Institute of Technology（清华大学自动化系; 电子科技大学成像探测与智能感知实验室; 北京理工大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外小目标检测，提出LCPNet。通过在潜在空间展开并利用低秩先验，推导LCP求解器及引入共享优化内存，实现准确鲁棒检测，优于现有方法。
AI 中文摘要
红外小目标检测旨在从复杂红外背景中识别远距离小目标。深度学习方法改进了该任务，但存在不足。本文提出LCPNet，验证低秩先验在潜在表示中有效并在此空间展开，推导求解器，引入共享优化内存，实验表明其性能优于现有方法。
英文摘要
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 targets and 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 task-adaptive 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 achieves accurate and robust detection with low false-alarm rates, together with competitive efficiency among high-accuracy deep unfolding methods. Model and code are available at Model and code are available at https://github.com/Tianfang-Zhang/LCPNet.
