发表机构
Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences; University of Chinese Academy of Sciences; Beijing Institute of Astronautical Systems Engineering; VCIP, College of Computer Science, Nankai University; NKIARI（中国科学院西安光学精密机械研究所; 中国科学院大学; 北京航天自动控制研究所; 南开大学计算机科学学院可视化与计算机图像处理实验室; 未提及具体中文名称）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究紧密间隔红外小目标解混问题，提出单阶段轻量级FOCUS方案，受图像超分辨率与CSIST解混同构退化模型启发，转换相关要素，避免几何恢复与伪像抑制纠缠，提升定位和解混精度，推理速度提高60倍。
AI 中文摘要
由于光学衍射极限和长成像距离，紧密间隔红外小目标（CSIST）通常表现出能量重叠，在红外图像中表现为难以区分的斑点。这种模糊性使传统检测的一对一映射假设无效，因此需要向CSIST解混范式转变，即将这些斑点分解为离散子目标。然而，主流的深度展开网络受限于其重复迭代架构固有的高延迟和结构不灵活性。为此，我们提出了快速单阶段CSIST解混方案（FOCUS），这是一种单阶段轻量级范式，表明深度展开并非必要。受图像超分辨率（SR）和CSIST解混共享同构退化模型这一关键观察的启发，我们的见解是，通过完全转换标签空间、损失函数和评估标准，可以实现从图像SR到CSIST解混的范式转变。具体而言，为避免几何恢复与伪像抑制纠缠，FOCUS采用单通道映射，内部有从粗到细的流，从粗空间分布逐步将目标定位细化到更精细的子像素精度。虽然稀疏正则化抑制背景杂波，但也会削弱目标强度。为补偿有效信号的这种衰减，引入通量守恒作为竞争约束，将信号能量恢复到目标中心。据我们所知，这项工作是首次尝试通过无深度展开网络范式的轻量级单阶段框架来解决此任务。实验表明，我们的方法在定位和解混精度上与或超过了当前最先进的展开方法，同时将推理速度提高了60倍。
英文摘要
Due to the optical diffraction limit and long imaging distances, Closely-Spaced Infrared Small Targets (CSIST) typically exhibit energy overlap, manifesting as indistinguishable blobs in infrared images. This ambiguity invalidates the one-to-one mapping assumption of traditional detection, thereby necessitating a paradigm shift towards CSIST Unmixing, which decomposes these blobs into discrete sub-targets. However, the dominant paradigm deep unfolding networks are shackled by the high latency and structural inflexibility intrinsic to their repetitively iterative architecture. To this end, we propose the Fast One-stage CSIST Unmixing Scheme (FOCUS), a one-stage lightweight paradigm which demonstrates that deep unfolding is not necessary. Motivated by the key observation that image super-resolution (SR) and CSIST Unmixing share an isomorphic degradation model, our insight is that it is possible to achieve a paradigm shift from image SR to CSIST Unmixing via completely transforming the label space, loss functions, and evaluation criteria. Specifically, to avoid entangling geometric recovery with artifact suppression, FOCUS adopts a single pass mapping with an internal coarse-to-fine flow that progressively refines target localization from coarse spatial distributions to finer sub-pixel precision. While sparsity regularization suppresses background clutter, it also attenuates target intensities. To compensate for this attenuation of valid signals, flux conservation is introduced as a competing constraint that restores signal energy back to target centers. To the best of our knowledge, this work is the first attempt to address this task via a lightweight one-stage framework without the DUN paradigm. Experiments demonstrate that our method matches or surpasses the state-of-the-art unfolding approaches in both localization and unmixing accuracy, while boosting the inference speed by 60x.
