Infrared Physics & Technology最新文献_第7页

Intelligent origin discrimination of Wuyi Rock Tea during storage using a time-spectral dual-dimensional transformer model 基于时间谱二维变压器模型的武夷岩茶储存期产地智能判别

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.infrared.2026.106411

Xue Bai , Min Sun , Xianshu Fu , Jinyan Liao , Mingzhou Zhang , Xiaoping Yu , Zihong Ye

This study addresses the challenge of accurately discriminating the origin of Wuyi Rock Tea due to dynamic changes in its compound composition during storage. A novel intelligent discrimination method based on a time-spectral dual-dimensional dynamic model is proposed. By integrating Near-Infrared (NIR) spectroscopy with machine learning techniques, Principal Component Analysis (PCA) combined with Isolation Forest (iForest) was employed to eliminate anomalous samples, while 12 single preprocessing methods and 6 hybrid combinations were introduced to enhance data quality. The performance of four traceability models—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Transformer—was evaluated, with a focus on optimizing the Transformer model through input dimension screening. The term ’time-spectral dual-dimensional’ denotes the fusion of storage time and spectral (NIR) data via the Transformer’s self-attention mechanism, capturing spectral evolution over time without employing time-series architectures. Results demonstrated that the optimized Transformer model combined with first-order derivative (1D) preprocessing (O-1D-Transformer) achieved the highest performance, with test set accuracy, Macro-F1, and G-mean values of 98.05%, 97.11%, and 98.41%, respectively. By dynamically modulating inter-band correlations via self-attention, this model captures storage-induced chemical patterns, distinguishing origin signals from chronological noise and thereby overcoming the time-blindness of static models. This research provides a new paradigm for intelligent traceability of time-sensitive food products, validating the synergistic advantages of NIR spectroscopy and deep learning, with significant implications for tea quality regulation and brand protection.

由于武夷岩茶在贮藏过程中化合物成分的动态变化，本研究解决了准确鉴别其产地的挑战。提出了一种基于时谱二维动态模型的智能识别方法。通过将近红外（NIR）光谱与机器学习技术相结合，采用主成分分析（PCA）与隔离森林（ifforest）相结合的方法来消除异常样本，并引入12种单一预处理方法和6种混合预处理方法来提高数据质量。评估了k -近邻（KNN）、支持向量机（SVM）、随机森林（RF）和Transformer四种可追溯模型的性能，重点是通过输入维度筛选优化Transformer模型。术语“时间光谱二维”表示通过Transformer的自关注机制将存储时间和光谱（NIR）数据融合，在不使用时间序列架构的情况下捕获随时间变化的光谱。结果表明，优化后的变压器模型结合一阶导数（1D）预处理（O-1D-Transformer）获得了最高的性能，测试集精度、Macro-F1和G-mean分别为98.05%、97.11%和98.41%。通过自关注动态调制带间相关性，该模型捕获了存储诱导的化学模式，将起源信号与时间噪声区分开来，从而克服了静态模型的时间盲目性。本研究为时间敏感型食品的智能溯源提供了一个新的范例，验证了近红外光谱和深度学习的协同优势，对茶叶质量监管和品牌保护具有重要意义。

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引用次数: 0

Quantile interval prediction strategy enhances quantitative characterization of mycotoxin content in wheat using Near-Infrared spectroscopy 分位区间预测策略增强了小麦真菌毒素含量的近红外光谱定量表征

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.infrared.2026.106441

Jingwen Zhu , Xuehao Shen , Xianjun Sun , Huazhi Wang , Hui Jiang

Rapid and reliable detection of mycotoxins in wheat is of great significance for ensuring food safety and risk warning. This study combines portable near-infrared spectroscopy technology with quantile regression deep learning (QRDL) to construct an efficient and information-rich method for predicting the range of mycotoxin content. This study compared classification models and point prediction regression models using convolutional neural networks (CNN) and long short-term memory (LSTM), as well as interval prediction models based on quantile regression convolutional neural networks (QR-CNN) and quantile regression long short-term memory (QR-LSTM). The results show that the interval prediction model demonstrates excellent performance in terms of interval width control, median fitting accuracy, and control of the number of samples with inter-class bias. It effectively enhances the ability to express uncertainties, and the interpretability of predictions. This study provides more robust information support for the identification of mycotoxin risk levels and on-site rapid detection in wheat.

快速、可靠地检测小麦中的真菌毒素，对确保食品安全和风险预警具有重要意义。本研究将便携式近红外光谱技术与分位数回归深度学习（QRDL）相结合，构建了一种高效、信息量丰富的霉菌毒素含量范围预测方法。本研究比较了基于卷积神经网络（CNN）和长短期记忆（LSTM）的分类模型和点预测回归模型，以及基于分位数回归卷积神经网络（QR-CNN）和分位数回归长短期记忆（QR-LSTM）的区间预测模型。结果表明，区间预测模型在区间宽度控制、中位数拟合精度、类间偏倚样本数量控制等方面均表现出良好的性能。它有效地提高了表达不确定性的能力和预测的可解释性。本研究为小麦霉菌毒素风险等级的鉴定和现场快速检测提供了更有力的信息支持。

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引用次数: 0

Intracavity switchable Laguerre–Gaussian mode generation in a side-pumped Nd:YAG laser via spot defect and off-axis pumping 利用光斑缺陷和离轴泵浦产生侧泵浦Nd:YAG激光器腔内可切换拉盖尔-高斯模式

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-01 DOI: 10.1016/j.infrared.2026.106366

Zeqian Liu , Hui Chen , Jiashuo An , Junhong Chen , Bingzheng Yan , Yulei Wang , Zhiwei Lu , Zhenxu Bai

Laguerre-Gaussian (LG) beams carrying orbital angular momentum (OAM) demonstrate enhanced information-carrying capacity, making them particularly valuable for high-capacity optical communications. Additionally, petal-like LG beams exhibit unique advantages for spin angular velocity measurements due to their distinctive intensity profiles and phase singularity characteristics. However, current laser systems face significant challenges in achieving direct switching between high-order vortex beams and petal-like beams outputs. This study presents a side-pumped Nd:YAG laser system capable of direct switching between high-order vortex beams and petal-like beams. By introducing specifically engineered spot defects of varying dimensions in the resonator mirrors of a side-pumped Nd:YAG laser system, we demonstrate tunable vortex beam generation from 1st to 6th order through cavity length adjustments. Furthermore, mode conversion between vortex and petal-like beams is achieved using an off-axis pumping scheme, in which the pump beam is intentionally displaced from the center of the defect. Using this approach, stable petal-like LG modes ranging from LG_0,1 to LG_0,12. are successfully generated. To the best of our knowledge, this work demonstrates for the first time an intracavity LG mode conversion strategy that combines engineered spot defects with off-axis pumping, providing a compact and versatile platform for flexible structured-light generation.

携带轨道角动量（OAM）的拉盖尔-高斯（LG）光束显示出增强的信息承载能力，使它们在高容量光通信中特别有价值。此外，花瓣状的LG光束由于其独特的强度分布和相位奇点特性，在自旋角速度测量中表现出独特的优势。然而，目前的激光系统在实现高阶涡旋光束和花瓣状光束输出之间的直接切换方面面临着重大挑战。本文提出了一种能够在高阶涡旋光束和花瓣状光束之间直接切换的侧泵浦Nd:YAG激光系统。通过在侧面泵浦Nd:YAG激光系统的谐振镜中引入特殊设计的不同尺寸的光斑缺陷，我们演示了通过腔长调节产生1到6阶的可调谐涡旋光束。此外，旋涡和花瓣状光束之间的模式转换采用了离轴泵浦方案，其中泵浦光束有意地从缺陷的中心偏移。利用这种方法，可以得到稳定的花瓣状LG模态，范围从LG0,1到LG0,12。生成成功。据我们所知，这项工作首次展示了一种结合了工程光斑缺陷和离轴泵浦的腔内LG模式转换策略，为灵活的结构光产生提供了一个紧凑而通用的平台。

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引用次数: 0

A novel diffusion-based background estimation for infrared dim small target detection 一种新的基于扩散的红外弱小目标检测背景估计

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-10 DOI: 10.1016/j.infrared.2026.106384

Sheng-hui Rong , Wang Zi-ming , Gao Xue-zhen , Zhao Wen-feng , Wu Xu-peng , Zhang Tao

Infrared small target detection is a crucial technique in the field of computer vision. With the advancement of deep learning, Convolutional Neural Network (CNN)-based methods have achieved promising results in target detection. However, due to the small size of the targets, relying solely on dense convolutional layers may lead to information loss. To address the issue of inaccurate background prediction in complex background, we propose an end-to-end infrared background prediction method based on a conditional diffusion model with an adaptive blocking strategy. On one hand, the adaptive blocking strategy effectively integrates both local and global information from the infrared image while significantly accelerating the inference speed of the diffusion model. On the other hand, the multi-scale attention segmentation module can effectively suppress background clutter and enhance the target. We also created an IRDF (infrared for diffusion) dataset, comprising of 23,378 images to evaluate the detection performance of the proposed method and the comparison methods. Extensive experiments demonstrate that our approach is capable of detecting targets precisely and performs effectively in various complex backgrounds.

红外小目标检测是计算机视觉领域的一项关键技术。随着深度学习的发展，基于卷积神经网络（CNN）的方法在目标检测方面取得了可喜的效果。然而，由于目标的体积较小，单纯依赖于密集卷积层可能会导致信息丢失。为了解决复杂背景下背景预测不准确的问题，提出了一种基于条件扩散模型的端到端红外背景预测方法。一方面，自适应分块策略有效地整合了红外图像的局部和全局信息，同时显著加快了扩散模型的推理速度；另一方面，多尺度注意力分割模块可以有效抑制背景杂波，增强目标。我们还创建了一个包含23,378张图像的IRDF（红外扩散）数据集，以评估所提出方法和比较方法的检测性能。大量的实验表明，我们的方法能够精确地检测目标，并且在各种复杂背景下都能有效地检测目标。

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引用次数: 0

HR-YOLO: Enhanced YOLO framework for infrared-based timber component damage detection in heritage arch lounge bridges HR-YOLO：基于红外的传统拱廊桥木材构件损伤检测的增强YOLO框架

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.infrared.2026.106401

Zhiqiang Qin , Qifang Xie , Zheng Li

This study proposes an infrared imaging-based damage detection method for ancient Chinese timber arch lounge bridges using an improved YOLO11 model. Three datasets were established for damage detection scenarios involving general timber components and heritage timber arch lounge bridge components. Although the YOLO11 model achieved good performance after hyperparameter optimization in general timber components detection task (69.3% on cracks; 63.2% on multi-damage), its precision dropped significantly in heritage timber arch lounge bridge scenarios due to small targets and complex backgrounds. A transfer learning strategy was adopted to overcome this limitation, which enhanced training stability and detection precision for heritage bridge damage detection. Moreover, Non-local Attention Mechanism and SimAM were integrated into the YOLO11 architecture to improve the model performance over three datasets. Detection results demonstrated that the Non-local Attention Mechanism achieves higher precision and is more suitable for infrared video detection tasks compared to the baseline model. SimAM exhibits significant lightweight characteristics while maintaining great precision. Finally, we outline a practical UAV-based inspection workflow that couples infrared data acquisition with real-time HR-YOLO inference to support repeatable, field-ready monitoring of heritage timber bridges. This research offers an automated solution for real-time structural health monitoring of cultural heritage, promoting the application of intelligent infrared detection techniques in heritage preservation and civil engineering.

本研究提出了一种基于红外成像的中国古代木拱廊桥损伤检测方法，该方法采用改进的YOLO11模型。建立了普通木材构件和传统木材拱廊桥构件损伤检测场景的3个数据集。尽管经过超参数优化后的YOLO11模型在一般木材构件检测任务中取得了较好的性能（裂缝检测69.3%，多损伤检测63.2%），但在传统木拱桥场景中，由于目标小、背景复杂，其精度明显下降。采用迁移学习策略克服了这一局限性，提高了传统桥梁损伤检测的训练稳定性和检测精度。此外，将非局部注意机制和SimAM集成到YOLO11体系结构中，提高了模型在三个数据集上的性能。检测结果表明，与基线模型相比，非局部注意机制具有更高的检测精度，更适合红外视频检测任务。SimAM具有显著的轻量化特性，同时保持极高的精度。最后，我们概述了一种实用的基于无人机的检测工作流程，该流程将红外数据采集与实时HR-YOLO推断相结合，以支持对传统木桥进行可重复的现场监测。本研究为文物结构健康实时监测提供了自动化解决方案，促进了智能红外探测技术在文物保护和土木工程中的应用。

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引用次数: 0

Multimodal online monitoring of laser melt pool based on polarized infrared spectral imaging 基于偏振红外光谱成像的激光熔池多模态在线监测

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI: 10.1016/j.infrared.2026.106404

Xianjin Kong, Guibo Chen, Ye Zhang

This study proposes a multimodal diagnostic approach that integrates infrared polarization spectroscopy imaging, spectral temperature inversion, and numerical simulation to investigate the dynamic evolution of melt pool behavior during laser processing. A near-infrared polarization imaging system was employed to achieve real-time observation of the melt pool interface and morphological evolution. By incorporating the multispectral thermometry (MST) method, temperature reconstruction was conducted, revealing a strong spatial correlation between the saturated regions in the degree of linear polarization (DOLP) images and localized high-temperature zones. To address the limitation of polarization imaging in capturing melt pool depth, a multiphysics model incorporating key mechanical mechanisms was established to analyze the coupled evolution of temperature and flow fields and to support interface morphology interpretation. Experimental results demonstrate that the interface contours extracted from DOLP images closely match the boundaries observed in thermal imaging, with deviations between experimental observations and numerical predictions remaining below 10%. In the quasi-steady-state regime, interfacial driving force analysis indicates that recoil pressure predominantly governs the formation of interfacial depressions, while Marangoni forces control the recirculation of molten metal and the flattening of edge morphology. Furthermore, the study reveals nonlinear thermal and morphological responses of the melt pool under varying combinations of pulse width and repetition rate. The integrated experimental–numerical framework developed in this work enables melt-pool behavior identification and mechanism interpretation, and provides a foundation for predictive analysis of interface evolution, offering a systematic basis for understanding interfacial dynamics in laser processing.

本研究提出了一种集成红外偏振光谱成像、光谱温度反演和数值模拟的多模态诊断方法，以研究激光加工过程中熔池行为的动态演变。采用近红外偏振成像系统对熔池界面和形态演变进行实时观测。结合多光谱测温（MST）方法进行温度重建，发现线偏振度（DOLP）图像中的饱和区域与局部高温区之间存在较强的空间相关性。为了解决极化成像在捕捉熔池深度方面的局限性，建立了包含关键力学机制的多物理场模型，分析了温度场和流场的耦合演化，并支持界面形态解释。实验结果表明，从DOLP图像中提取的界面轮廓与热成像中观测到的边界吻合较好，实验观测值与数值预测值的偏差小于10%。在准稳态状态下，界面驱动力分析表明，反冲压力主要控制界面凹陷的形成，而Marangoni力主要控制熔融金属的再循环和边缘形貌的平坦化。此外，研究还揭示了不同脉冲宽度和重复频率组合下熔池的非线性热响应和形态响应。本研究开发的综合实验-数值框架使熔池行为识别和机制解释成为可能，并为界面演变的预测分析奠定了基础，为理解激光加工中的界面动力学提供了系统的基础。

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引用次数: 0

DTRDM: Dual-task directional residual denoising diffusion model for multimodal image fusion 多模态图像融合的双任务定向残差去噪扩散模型

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-07 DOI: 10.1016/j.infrared.2026.106389

Jinlin Jiang , Gang Hu , Guanglei Sheng , Guo Wei

Image fusion enhances complementary details and visual quality by integrating information from multiple modalities, thereby supporting more accurate decision-making in downstream tasks. While diffusion models show strong generative ability in fusion tasks, the absence of real-image supervision restricts their ability to capture local features. To address this, we propose a Dual-Task Directed Residual Denoising Diffusion Model (DTRDM) to better capture multi-scale diffusion features and enrich fused image content. First, we introduce two diffusion biases: “image residuals and pure noise” to guide forward diffusion in a goal-oriented manner. This strategy explicitly guides the inverse fusion process while reducing training complexity. Second, we design a noise prediction module based on a dual U-Net architecture, which generates residual or noise prediction features depending on the training objective. Multi-scale features are refined through cascading and iterative extraction, enabling the model to capture local details across modalities and enhance the fused representation. Finally, we introduce a color–structure-preserving composite loss for denoising, which strengthens feature complementarity across scales. Extensive experiments show that DTRDM achieves state-of-the-art results across key metrics with strong adaptability. It generalizes to diverse fusion tasks without retraining, and its results substantially improve decision-making in applications such as autonomous driving, traffic monitoring, and medical imaging.

图像融合通过整合来自多种模式的信息来增强互补细节和视觉质量，从而支持下游任务更准确的决策。虽然扩散模型在融合任务中表现出较强的生成能力，但缺乏真实图像的监督限制了其捕捉局部特征的能力。为了解决这一问题，我们提出了一种双任务定向残差去噪扩散模型（DTRDM），以更好地捕捉多尺度扩散特征，丰富融合图像内容。首先，我们引入两种扩散偏差：“图像残差和纯噪声”，以目标导向的方式引导向前扩散。该策略明确地指导了逆融合过程，同时降低了训练复杂度。其次，设计基于双U-Net架构的噪声预测模块，根据训练目标生成残差或噪声预测特征；通过级联和迭代提取来细化多尺度特征，使模型能够跨模态捕获局部细节，增强融合表示。最后，我们引入了一种保持颜色结构的复合损失去噪方法，增强了尺度上的特征互补性。大量的实验表明，DTRDM在关键指标上取得了最先进的结果，具有较强的适应性。它可以推广到不同的融合任务，而无需再训练，其结果大大提高了自动驾驶、交通监控和医疗成像等应用中的决策能力。

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引用次数: 0

HFFN: Hierarchical feature fusion network for RGB-infrared object detection HFFN: rgb -红外目标检测的分层特征融合网络

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-26 DOI: 10.1016/j.infrared.2026.106429

Jinhang Zhang, Min Gao, Yi Wang, Chaowang Li, Dan Fang, Zhuang Wei

In recent years, multimodal object detection techniques based on visible(RGB) and infrared(IR) images have gained attention as a research hotspot. These techniques leverage the complementary characteristics of RGB and IR images to overcome the environmental adaptability limitations of single-modality methods. The fusion of RGB and IR images is crucial for object detection under complex lighting conditions. By effectively integrating the complementary information of the two modality images, the robustness and accuracy of detection are enhanced. However, current image fusion methods often encounter issues of inaccurate modality alignment and insufficient representation of fused features during feature extraction and fusion. We propose a Hierarchical Feature Fusion Network (HFFN) that achieves efficient extraction and accurate alignment of multimodal features using an Adaptive Pooling Transformer (APT) and an Invertible Detail Extraction (IDE), significantly enhancing the fusion quality. The APT combines pooling operations and the Transformer structure, enabling it to capture global information and expand the receptive field of features; The IDE focuses on extracting local detail features, including edges and texture information, to enhance the fineness and clarity of the fused image. We constructed object detectors based on the HFFN to validate its effectiveness. Extensive experiments on datasets demonstrate the superior performance of our method.

近年来，基于可见光（RGB）和红外（IR）图像的多模态目标检测技术作为一个研究热点备受关注。这些技术利用RGB和IR图像的互补特性来克服单模态方法的环境适应性限制。RGB和IR图像的融合对于复杂光照条件下的目标检测至关重要。通过有效地整合两模态图像的互补信息，提高了检测的鲁棒性和准确性。然而，目前的图像融合方法在特征提取和融合过程中经常遇到模态对齐不准确和融合特征表示不充分的问题。我们提出了一种分层特征融合网络（HFFN），该网络利用自适应池化变压器（APT）和可逆细节提取（IDE）实现了多模态特征的高效提取和精确对齐，显著提高了融合质量。APT结合了池化操作和Transformer结构，使其能够捕获全局信息并扩展特征的接受域；IDE专注于提取局部细节特征，包括边缘和纹理信息，以增强融合图像的精细度和清晰度。我们构建了基于HFFN的目标检测器来验证其有效性。大量的数据集实验证明了该方法的优越性能。

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引用次数: 0

SamFusion:A model for multimodal image fusion guided by SAM’s rich semantics SamFusion：基于SAM丰富语义的多模态图像融合模型

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.infrared.2026.106438

Yucheng Zhang, You Ma, Lin Chai

Infrared and visible image fusion aims to combine the advantages of each modality, such as salient targets in infrared images and detailed textures in visible images, in order to generate images with better visual effects and richer information. Existing fusion methods do not distinguish between semantic and texture information, ignoring their specific roles in the image fusion process, whereas we have demonstrated the critical role of semantic information in the image fusion task. However, the semantic information extraction networks of current methods are restricted to be trained on limited infrared and visible datasets, which results in significantly inadequate semantic information extraction capabilities when facing complex scenes. To address this challenge, we pioneered SamFusion, an image fusion model that leverages the SAM large model, which effectively exploits the rich semantic information in SAM to facilitate image fusion. Specifically, we introduce the Multi Level Semantic Aggregation (MLSA) and Soft Interaction (SI) modules, which aim to mine the semantic information at each level in SAM that is conducive to fusion, while reducing the modality differences in semantic information. In addition, specialized fusion modules are designed for semantic information and texture information respectively. For texture information with significant modality differences, semantic information is used as a guide for selective fusion. Extensive experiments show that our method outperforms the state-of-the-art methods in highlighting salient targets and detailed textures. In addition, the effectiveness of our method is revealed in medical image fusion and downstream tasks of object detection and semantic segmentation.

红外图像与可见光图像融合的目的是将红外图像中突出的目标和可见光图像中细致的纹理等各自的优点结合起来，生成视觉效果更好、信息更丰富的图像。现有的融合方法没有区分语义信息和纹理信息，忽略了它们在图像融合过程中的具体作用，而我们已经证明了语义信息在图像融合任务中的关键作用。然而，当前方法的语义信息提取网络仅限于在有限的红外和可见光数据集上进行训练，这导致在面对复杂场景时语义信息提取能力明显不足。为了应对这一挑战，我们首创了SamFusion，这是一种利用SAM大模型的图像融合模型，它有效地利用SAM中丰富的语义信息来促进图像融合。具体来说，我们引入了多层语义聚合（MLSA）和软交互（SI）模块，旨在挖掘SAM中每一层有利于融合的语义信息，同时减少语义信息的模态差异。此外，还分别针对语义信息和纹理信息设计了专门的融合模块。对于情态差异较大的纹理信息，以语义信息为指导进行选择性融合。大量的实验表明，我们的方法在突出突出目标和细节纹理方面优于最先进的方法。此外，我们的方法在医学图像融合以及后续的目标检测和语义分割任务中也显示了有效性。

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引用次数: 0

Computational spectral reconstruction using a Quasi-random metasurface filter array for LWIR (8–12 μm) band 基于准随机超表面滤波阵列的LWIR （8-12 μm）波段计算光谱重建

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-03-01 Epub Date: 2026-01-27 DOI: 10.1016/j.infrared.2026.106418

Lingfeng Zhang , Yu Shao , Fuyan Wu , Luoyu Zhang , Yipeng Chen , Weiming Shi , Junren Wen , Yuchuan Shao , Weidong Shen , Chenying Yang

Conventional Fourier-transform infrared (FTIR) spectrometers face inherent limitations due to their need for precise mechanical scanning and fundamental size constraints from mirror displacement requirements, which collectively hinder its miniaturization. However, in certain application domains, the importance of spectrometer miniaturization outweighs the need for high spectral resolution or a broad spectral range. In this work, we present a highly compact metasurface-based infrared spectrometer that leverages deep learning for spectral reconstruction. The system employs 24 distinct Quasi-Random Metasurface Spectra-Encoders (QRM-SEs), each exhibiting unique spectral characteristics. To achieve accurate spectral reconstruction, a fully connected spectral reconstruction network is applied with diverse datasets as the training datasets, ensuring strong generalization across various spectral types. By utilizing the QRM-SEs, high-fidelity reconstruction of spectral information in the 8–12 μm band has been successfully achieved. This miniaturized spectrometer demonstrates strong potential for integration into compact devices and holds broad application prospects in areas such as consumer electronics, environmental monitoring, and portable sensing technologies.

传统的傅里叶变换红外（FTIR）光谱仪由于需要精确的机械扫描和反射镜位移要求的基本尺寸限制而面临固有的局限性，这些限制共同阻碍了其小型化。然而，在某些应用领域，光谱仪小型化的重要性超过了对高光谱分辨率或宽光谱范围的需求。在这项工作中，我们提出了一种高度紧凑的基于超表面的红外光谱仪，它利用深度学习进行光谱重建。该系统采用了24个不同的准随机超表面光谱编码器（qrm - se），每个编码器都具有独特的光谱特征。为了实现精确的光谱重建，采用全连接的光谱重建网络，以不同的数据集作为训练数据集，保证了不同光谱类型的强泛化。利用QRM-SEs，成功实现了8 ~ 12 μm波段的高保真光谱信息重建。这种小型化光谱仪显示了集成到小型设备中的强大潜力，在消费电子、环境监测和便携式传感技术等领域具有广泛的应用前景。

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引用次数: 0