\textcolor{blue}{This is the pre-acceptance version, to read the final version please go to \href{https://ieeexplore.ieee.org/document/11156113}{IEEE Transactions on Geoscience and Remote Sensing on IEEE Xplore}.} Infrared small target detection (IRSTD) remains a long-standing challenge in complex backgrounds due to low signal-to-clutter ratios (SCR), diverse target morphologies, and the absence of distinctive visual cues. While recent deep learning approaches aim to learn discriminative representations, the intrinsic variability and weak priors of small targets often lead to unstable performance. In this paper, we propose a novel end-to-end IRSTD framework, termed LRRNet, which leverages the low-rank property of infrared image backgrounds. Inspired by the physical compressibility of cluttered scenes, our approach adopts a compression--reconstruction--subtraction (CRS) paradigm to directly model structure-aware low-rank background representations in the image domain, without relying on patch-based processing or explicit matrix decomposition. To the best of our knowledge, this is the first work to directly learn low-rank background structures using deep neural networks in an end-to-end manner. Extensive experiments on multiple public datasets demonstrate that LRRNet outperforms 38 state-of-the-art methods in terms of detection accuracy, robustness, and computational efficiency. Remarkably, it achieves real-time performance with an average speed of 82.34 FPS. Evaluations on the challenging NoisySIRST dataset further confirm the model's resilience to sensor noise. The source code will be made publicly available upon acceptance.

翻译：\textcolor{blue}{此为预接收版本，阅读最终版本请访问 \href{https://ieeexplore.ieee.org/document/11156113}{IEEE Xplore 上的 IEEE Transactions on Geoscience and Remote Sensing}。} 在复杂背景下，由于信杂比低、目标形态多样且缺乏显著视觉特征，红外小目标检测一直是一项长期存在的挑战。尽管近期的深度学习方法旨在学习判别性表示，但小目标固有的多变性和弱先验常导致性能不稳定。本文提出了一种新颖的端到端红外小目标检测框架，称为 LRRNet，该框架利用了红外图像背景的低秩特性。受杂波场景物理可压缩性的启发，我们的方法采用压缩-重构-减影范式，直接在图像域中建模结构感知的低秩背景表示，而无需依赖基于块的处理或显式矩阵分解。据我们所知，这是首个利用深度神经网络以端到端方式直接学习低秩背景结构的工作。在多个公开数据集上的大量实验表明，LRRNet 在检测精度、鲁棒性和计算效率方面优于 38 种最先进的方法。值得注意的是，它以平均 82.34 FPS 的速度实现了实时性能。在具有挑战性的 NoisySIRST 数据集上的评估进一步证实了该模型对传感器噪声的鲁棒性。源代码将在论文被接受后公开。