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4th International Conference on Electronic Information and Communication Engineering, EICE 2025

作者： Wang, Haoxu Li, Bingjie Liu, Ming Yang, Dezhen Chen, Zhijie Liu, Xu Wan, Peiyuan Beijing Embedded System Key Lab School of Information Science and Technology Beijing University of Technology Beijing China National Key Laboratory of Infrared Detection Technologies Beijing China

This article offers a detailed exploration of the Normalized Least Mean Squares (NLMS) algorithm, highlighting its enhanced adaptive filtering capabilities and robust performance in managing input signal correlations. It delves into the foundational principles of the NLMS algorithm and outlines the essential modules required for its implementation in Verilog HDL. The implementation and simulation of the NLMS adaptive filter are studied. The simulation outcomes indicate that the convergence rate of the algorithm is superior to that of the conventional LMS algorithm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Adaptive filtering

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infrared small target detection based on spatial attention density peaks searching

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Optoelectronics Letters 2025年第2期21卷 90-96页

作者： RAO Junmin LU Siyu LIU Shijian LI Fanming CAS Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical PhysicsShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China

The detection of small targets poses a significant challenge for infrared search and tracking (IRST) systems,as they must strike a delicate balance between accuracy and *** this paper,we propose a detection algorithm based on spatial attention density peaks searching (SADPS) and an adaptive window selection ***,the difference-of-Gaussians (DoG) filter is introduced for preprocessing raw infrared ***,the image is processed by ***,an adaptive window selection scheme is applied to obtain window templates for the target scale ***,the small target feature is used to enhance the target and suppress the ***,the true targets are segmented through a *** experimental results show that compared with the seven state-of-the-art small targets detection baseline algorithms,the proposed method not only has better detection accuracy,but also has reasonable time consumption.

关键词： template searching infrared

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infrared aircraft few-shot classification method based on cross-correlation network

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红外与毫米波学报 2025年第1期44卷 103-111页

作者： HUANG Zhen ZHANG Yong GONG Jin-Fu Key Laboratory of Infrared System Detection and Imaging Technology Chinese Academy of SciencesShanghai 200083China University of Chinese Academy of Sciences Beijing 100049China Shanghai Institute of Technical Physics Chinese Academy of SciencesShanghai 200083China

In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is *** method combines two core modules:a simple parameter-free self-attention and *** analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot *** proposed cross-correlation network integrates these two modules and is trained in an end-to-end *** simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between *** with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target *** results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.

关键词： infrared imaging aircraft classification few-shot learning parameter-free attention cross attention

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Evaluation of occlusion immunities of free-space orbital angular momentum shift keying

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Optics Express 2025年第6期33卷 13494-13505页

作者： Yang, Jinyu Zhang, Shurui Li, Lang Gao, Mingwei Gao, Chunqing Fu, Shiyao School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Key Laboratory of Information Photonics Technology Ministry of Industry and Information Technology of the People’s Republic of China Beijing100081 China Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of the People’s Republic of China Beijing100081 China National Key Laboratory on Near-Surface Detection Beijing100072 China

Orbital angular momentum (OAM) provides a new high-dimensional degree-of-freedom for lasers enabling high-dimensional shift keying, thus showing huge potential in free-space optical data-transmissions. Ensuring the accuracy of data transmission is of concern in any communication system. However, in the free-space OAM shift keying link, interferences from environmental occlusions will broaden the OAM spectrum and thus affect the correctness of information received. In this paper, the occlusion immunities of two commonly used OAM shift keying strategies—OAM-encoding-symbols and OAM-encoding-bits—are evaluated under different occlusion scenarios. We introduce radial, azimuthal, and random occlusions into the link and then analyze the bit error rate (BER) of various OAM shift keying strategies. The results illustrate that the strategy of OAM-encoding-symbols exhibits stronger occlusion immunity with zero BER than OAM-encoding-bits. Moreover, proof-of-concept experiments are done to transmit grayscale images under occlusions. The final decoding results show identical data-transmission performance with simulations. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Bit error rate

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infrared Dim Small Target detection Algorithm Based on Adaptive Attention Transformer Network

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Journal of Shanghai Jiaotong University (Science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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Selective formation of generalized optical skyrmions

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Optics Letters 2025年第9期50卷 2840-2843页

作者： Zhang, Haojun Gao, Chunqing Fu, Shiyao School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Key Laboratory of Information Photonics Technology Ministry of Industry and Information Technology of the People’s Republic of China Beijing100081 China Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of the People’s Republic of China Beijing100081 China National Key Laboratory on Near-Surface Detection Beijing100072 China

Optical skyrmions, a type of topologically protected quasiparticle, have undergone various deformations in recent years. In this Letter, we present a scheme that can achieve the selective formation of generalized optical skyrmions. In our proposal, a liquid-crystal spatial light modulator and a variable liquid-crystal retarder, both of which are commonly programmable devices, are employed, allowing for the on-demand precise manipulation of topological textures of various quasiparticles including but not limited to skyrmions, antiskyrmions, and bimerons. The proof-of-concept experiments illustrate that generalized optical skyrmions whose skyrmion number up to 5 can be well generated with on-demand tailored topological textures. While maintaining a simple setup, the system demonstrates a notable capacity for the manipulation of intricate high-order textures, signifying its considerable potential for extensive and sophisticated applications in the generation and manipulation of skyrmions. © 2025 Optica Publishing Group.

关键词： Topology

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Temporal multiplexing coding strategy for spatiotemporal compressed imaging 4

Temporal multiplexing coding strategy for spatiotemporal com...

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4th International Computational imaging Conference, CITA 2024

作者： Hao, Xiaowen Ke, Jun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Key Laboratory of Photo-electronic Imaging Technology and System Ministry of Education of China Beijing100081 China National Key Laboratory on Near-surface Detection Beijing100072 China

ISBN: (纸本)9781510688834

Spatiotemporal compressed imaging(STCI) aims to improve the spatial and temporal resolution of an imaging system simultaneously, which can effectively alleviate the mutual constraints between the two imaging resolutions. However, the development of STCI is limited by the effectively modulate target scenes in dual-domain, and by the back-end reconstruction process which doesn't work well for high compression ratio(CR). In this paper, we propose a temporal multiplexing coding strategy for STCI, which can effectively help the reconstruction algorithm for a large CR. Simulated experiments are implemented to demonstrate our idea. The proposed method can achieve nice spatiotemporal reconstructions with CR equal to 128:1, in which the spatial CR is 16:1 and the temporal CR is 8:1. © 2025 SPIE.

关键词： Image coding

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Optimizing object classification with single-pixel imaging and transformer networks 4

Optimizing object classification with single-pixel imaging a...

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4th International Computational imaging Conference, CITA 2024

作者： Cheng, Yin Liao, Yusen Sun, Xin Ke, Jun School of Optics and Photonics Beijing Institute of Technology Beijing100081 China Key Laboratory of Photo-electronic Imaging Technology and System Ministry of Education of China Beijing100081 China National Key Laboratory on Near-surface Detection Beijing100072 China

ISBN: (纸本)9781510688834

Traditional computer vision tasks rely on high-quality images for classification, but single-pixel imaging (SPI) uses compressive sensing with a bucket detector to simplify hardware and reduce costs. However, factors like atmospheric turbulence can degrade SPI image quality, lowering classification accuracy. To address this, a block-based single-pixel image-free transformer classification framework is proposed. It uses a learnable binary sampling matrix for flexible sampling during SPI and divides the measurements into blocks, which are processed by a transformer network. Each block is treated as a sequence element, allowing the transformer to capture spatial and contextual relationships. Experiments show strong robustness to atmospheric turbulence, achieving 87% classification accuracy even at a 1% sampling rate, making this method suitable for challenging environments. © 2025 SPIE.

关键词： Pixels

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Research on an automatic light field refocusing method for a camera array

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Optics Express 2025年第10期33卷 20964-20980页

作者： Pan, Yihua Zhao, Yushan Li, Yuyang Liu, Bin Chen, Ping Shanxi Key Laboratory of Intelligent Detection Technology & Equipment School of Information and Communication Engineering North University of China Shanxi Taiyuan 030051 China Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing 100081 China State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instruments North University of China Shanxi Taiyuan 030051 China

Light field information includes the position and angle information of light in space. A camera array is one of the methods for obtaining light field information, which combines and arranges multiple cameras in space to achieve multi-angle imaging of the same target. Based on the light field information, a computational imaging method can be used to achieve refocusing technology for a camera array (also known as optical synthetic aperture imaging technology). In this technology, by setting the focusing coefficient, the light field information can be focused on different depths. This mode is called taking photos first and focusing later. However, in order to focus on a specific object surface, traditional refocusing methods require users to manually adjust the focusing coefficient multiple times, which is time-consuming and user-unfriendly. Therefore, in this paper, we propose an automatic focusing method based on the optimal focusing coefficient map (OFCM) to address the manual refocusing problem. This method transforms the refocusing problem into obtaining the optimal focus coefficient map. In the process of solving OFCM, the threshold filtering method and the neighborhood filling method are used to optimize the results and improve the robustness of the proposed method. The experimental results show that our proposed method has higher imaging quality and faster running efficiency compared to automatic refocusing methods based on disparity maps and gradient functions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Biomedical imaging Computational imaging imaging techniques Plenoptic imaging Point spread function Real time imaging

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Feature Enhancement Network for infrared Small Target detection in Complex Backgrounds Based on Multi-Scale Attention Mechanism

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Applied Sciences (Switzerland) 2025年第9期15卷 4966-4966页

作者： Zhang, Sen Du, Weilin Liu, Yuan Zhou, Ni Li, Zheng Key Laboratory of Infrared System Detection and Imaging Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai200083 China University of Chinese Academy of Sciences Beijing100049 China

The identification of tiny objects via single-frame infrared is a significant challenge in computer vision, primarily due to large variances in target dimensions, overcrowded backgrounds, suboptimal signal-to-noise ratios, the propensity of bounding box regression to vary with target size, and potential partial occlusion scenarios. Addressing these challenges, we propose a sturdy network for enhancing features in the infrared detection of small targets utilizing multi-scale attention. In particular, the introduction of the Iterative Attentional Feature Fusion (iAFF) module at the detection network’s neck aims to tackle the issue of minor target features being overshadowed in the process of cross-scale feature fusion. Additionally, we present the Occlusion-Aware Attention Module (OAAM), which demonstrates greater tolerance for target localization errors in regions where local features are missing due to partial occlusion. By combining the scale and spatial attention mechanisms of the Dynamic Head, our approach adaptively learns the relative importance of different semantic layers. Furthermore, the integration of Normalized Wasserstein–Gaussian Distance (NWD) aims to tackle the convergence issues associated with the increased sensitivity of bounding box regression in identifying minor infrared targets. For assessing our technique’s efficiency, we present a novel benchmark dataset, IRMT-UAV, noted for its considerable discrepancies in target size, intricate backdrops, and substantial variations in the signal-to-noise ratio. The outcomes of our experiments using the public IRSTD-1k dataset and the internally developed IRMT-UAV dataset reveal that our technique surpasses cutting-edge (SOTA) methods, with mAP50 enhancements of 1.4% and 4.9%, respectively, thus proving our method’s efficiency and sturdiness. © 2025 by the authors.

关键词： Radar target recognition

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