穿透噪声：从噪声抑制视角改进红外小目标检测与分割
Seeing Through the Noise: Improving Infrared Small Target Detection and Segmentation from Noise Suppression Perspective
把频域低通/高通分解引入 FPN：用低频引导的高频分量净化来抑制噪声特征，并用螺旋采样替代规则网格采样做特征融合，可直接插到现有红外小目标检测/分割骨干上降低虚警。
原文依据 we pioneer in improving performance from noise suppression perspective and propose a novel noise-suppression feature pyramid network (NS-FPN)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论红外小目标检测与分割（IRSTDS）在国防与民用领域是一项关键而富有挑战性的任务，其原因在于目标外观暗淡、缺乏形状，且背景杂波严重。近年来的基于 CNN 的方法在目标感知上取得了有前景的结果，但它们仅专注于增强特征表示以抵消噪声的影响，从而导致虚警问题加剧。本文通过从频域分析该问题，率先从噪声抑制视角改进性能，提出了一种新颖的噪声抑制特征金字塔网络（NS-FPN），它在原始 FPN 结构中集成了低频引导的特征净化（LFP）模块与螺旋感知的特征采样（SFS）模块。LFP 模块通过净化高频分量来抑制噪声特征，从而实现不受噪声干扰的特征增强；SFS 模块则在特征融合过程中进一步采用螺旋采样来融合与目标相关的特征。我们的 NS-FPN 设计轻量而有效，可便捷地插入现有 IRSTDS 框架。在 IRSTD-1k 与 NUAA-SIRST 数据集上的大量实验表明，本方法显著降低了虚警，并在 IRSTDS 任务上取得了更优的性能。
英文原文摘要 1343 字符
Infrared small target detection and segmentation (IRSTDS) is a critical yet challenging task in defense and civilian applications, owing to the dim, shapeless appearance of targets and severe background clutter. Recent CNN-based methods have achieved promising target perception results, but they only focus on enhancing feature representation to offset the impact of noise, which results in the increased false alarm problem. In this paper, through analyzing the problem from the frequency domain, we pioneer in improving performance from noise suppression perspective and propose a novel noise-suppression feature pyramid network (NS-FPN), which integrates a low-frequency guided feature purification (LFP) module and a spiral-aware feature sampling (SFS) module into the original FPN structure. The LFP module suppresses the noise features by purifying high-frequency components to achieve feature enhancement devoid of noise interference, while the SFS module further adopts spiral sampling to fuse target-relevant features in feature fusion process. Our NS-FPN is designed to be lightweight yet effective and can be easily plugged into existing IRSTDS frameworks. Extensive experiments on the IRSTD-1k and NUAA-SIRST datasets demonstrate that our method significantly reduces false alarms and achieves superior performance on IRSTDS task.