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Recognition of UAVs in Infrared Images Based on YOLOv8

IEEE ACCESS 2025年 13卷 1534-1545页

作者： Zhou, Gang Liu, Xiuqi Bi, Hongliang Naval Univ Engn Sch Elect Engn Wuhan 430033 Hubei Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Jiangsu Peoples R China

With the advancement of Unmanned Aerial Vehicle (UAV) technology, there has been a notable increase in the utilization of UAVs by criminals for engaging in illegal activities, which poses significant threats to critical infrastructure and national security. Previous studies have predominantly relied on visible image recognition, which is vulnerable to variations in lighting conditions. Additionally, the performance of detection models is often hindered by the poor quality of images capturing UAV small targets. In contrast to visible light images, infrared images can capture the heat emitted by UAVs, rendering them less susceptible to fluctuations in lighting. To address the necessity for micro UAV detection in complex environments, this paper proposes an infrared image UAV detection method that employs super-resolution reconstruction through the Super-Resolution Convolutional Neural Network (SRCNN) and YOLOv8. Initially, a convolutional neural network is utilized to enhance the resolution and clarity of the infrared images. Subsequently, the YOLOv8 target detection model is applied to identify and detect UAVs. The results indicate that the average accuracy in detecting UAVs is 93.1%, which proves that it has excellent detection performance.

关键词： Autonomous aerial vehicles Accuracy Superresolution Feature extraction YOLO Image reconstruction Convolution Signal resolution reconstruction algorithms Data models UAV infrared image SRCNN YOLOv8 object detection super-resolution image recognition

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Hybrid Domain Enhancement Network for Ultraclean Pure Shift Spectroscopy With Fast Acquisition

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Shen, Jia Huang, Mingkai Chen, Yida Wu, Liubin Li, Hong Yang, Yu Chen, Zhong Xiamen Univ Dept Elect Sci Xiamen 361005 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China Jimei Univ Sch Ocean Informat Engn Xiamen 361021 Peoples R China

Pure shift technique can greatly improve the resolution of proton spectra compared to conventional nuclear magnetic resonance (NMR) spectroscopy but at the cost of longer acquisition time. To accelerate data acquisition in pure shift experiments, nonuniform sampling (NUS) is usually incorporated along with advanced data processing methods to reconstruct the spectrum. While some deep learning (DL) approaches have been proposed for this purpose, their performance declines when dealing with data under varying experimental conditions, primarily because they solely rely on frequency-domain information. To overcome this limitation, we develop a novel hybrid domain enhancement (HDE) network that combines time- and frequency-domain signals as input. We introduced an adaptive-domain transform unit (ADTU) to extract time-domain features that capture segmental correlations while retaining some temporal sequential information. Our experiments demonstrate that the proposed network effectively suppresses chunking sidebands and undersampling artifacts, showing exceptional robustness in complex scenarios, particularly under phase distortion. On real experimental data, it achieves a 5%-33% higher precision than state-of-the-art (SOTA) methods, indicating fewer artifacts in the reconstructed spectrum. This approach holds great potential for broader application in NMR spectroscopy, offering clearer and more accurate spectral interpretation.

关键词： Nuclear magnetic resonance Time-domain analysis Image reconstruction Couplings Amplitude modulation Spectroscopy Phase distortion Discrete Fourier transforms Time-frequency analysis reconstruction algorithms Chunking sideband neural network nonuniform sampling (NUS) pure shift signal reconstruction

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A Physics-Assisted Deep-Learning Scheme Based on Globally Perceptive Modules for Electromagnetic Inverse Scattering Problems

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Du, Changlin Pan, Jin Yang, Deqiang Hu, Jun Nie, Zaiping Chen, Yongpin Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

The multiple scattering effect is commonly present in electromagnetic (EM) scattering and can serve as a priori information to guide the solution of inverse scattering problems (ISPs). As depicted by the Green's function, the multiple scattering effect exhibits both global and local features, i.e., the interaction between subscatterers is in principle global but the coupling strength is usually large in the neighboring region and decays with distance. For solving ISPs, the convolutional layer (CL) has been proven to be highly effective as a fundamental building block in learning-based solvers. Through supervised learning, CL-based networks can predict the refined result from the pre-solution of EM parameters. However, due to the limitation of local perception capability, CL can only model the local feature of the multiple scattering effect. To remedy this, this article proposes a globally perceptive (GP) module that cascades a global self-attention layer (GSAL) and a CL to capture both global interaction and local emphasis. To show the effectiveness of the GP module, a deep-learning scheme with global perception (DLSGP) is constructed using a U-shape structure, where the pre-solution is provided by the backpropagation (BP) or domain current (DC) method. Validation results using cross-set data, noisy data, and measured data demonstrate the high accuracy, strong generalization ability, and robustness of the proposed module. The study on the global perception ability further confirms that the proposed module contains more information on the multiple scattering effect. Since this effect is common in ISPs, this fundamental module can be applied to other learning-based solvers.

关键词： Scattering Neural networks Learning systems Accuracy Vectors Iterative methods Inverse problems Electromagnetic scattering Robustness reconstruction algorithms Deep learning global self-attention globally perceptive (GP) module inverse scattering problems (ISPs) multiple scattering effect

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Multi-Scale Channel Distillation Network for Image Compressive Sensing

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IEEE ACCESS 2025年 13卷 9524-9537页

作者： Zhang, Tianyu Ye, Kuntao Zhang, Yue Lu, Rui Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China State Grid Xinyu Power Supply Co Elect Power Commun Dept Xinyu 338000 Peoples R China

Recently, convolutional neural networks (CNNs) have demonstrated striking success in computer vision tasks. Methods based on CNNs for image compressive sensing (CS) have also gained prominence. However, existing methods tend to increase the depth of the network in feature space for better reconstruction quality, neglecting the hierarchical representation of intermediate features in pixel space. In order to coordinate the feature space and pixel space to complete the deep reconstruction of images, and further improve the reconstruction performance of current CS methods, we propose a multi-scale channel distillation network (MSCDN). This network first obtains images of multiple scales using a scale-space image decomposition method at the sampling stage, followed by sampling these decomposed images through a convolutional operation. In this way, multi-scale information in the compressed domain is aggregated. During the reconstruction phase, a low-frequency information recovery network generates a preliminary image, whereas a high-frequency feature aggregation network refines the image further. Specifically, we design a dual-branch deep reconstruction architecture with channel distillation residual block (CDRB) as the core component. One branch extracts features gradually by cascading multiple CDRB modules, thereby supplementing the initial reconstructed image with a large amount of high-frequency content in feature space. The other branch takes the initial reconstructed image as input and sequentially fuses the intermediate feature outputs by CDRBs to increase the local details of the image in pixel space. Combining outputs from both branches, we achieve an optimal reconstructed image. Extensive experimental results on four benchmark datasets demonstrate that MSCDN surpasses state-of-the-art CS methods not only in reconstruction accuracy but also in perceptual visual quality.

关键词： Image reconstruction Feature extraction Convolution reconstruction algorithms Image coding Computational complexity Compressed sensing Training Optimization Matched filters Convolutional neural networks compressive sensing intermediate feature multi-scale image reconstruction

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Acoustic Resolution Photoacoustic Microscopy Imaging Enhancement: Integration of Group Sparsity With Deep Denoiser Prior

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2025年 34卷 522-537页

作者： Zhang, Zhengyuan Pan, Zuozhou Lin, Zhuoyi Sharma, Arunima Lin, Chia-Wen Pramanik, Manojit Zheng, Yuanjin Nanyang Technol Univ Sch Elect & Elect Engn Jurong West 639798 Singapore China Jiliang Univ Coll Metrol & Measurement Engn Hangzhou 314423 Peoples R China ASTAR Inst Infocomm Res Singapore 138632 Singapore Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21218 USA Natl Tsing Hua Univ Dept Elect Engn Hsinchu 300 Taiwan Iowa State Univ Dept Elect & Comp Engn Ames IA 50011 USA

Acoustic resolution photoacoustic microscopy (AR-PAM) is a novel medical imaging modality, which can be used for both structural and functional imaging in deep bio-tissue. However, the imaging resolution is degraded and structural details are lost since its dependency on acoustic focusing, which significantly constrains its scope of applications in medical and clinical scenarios. To address the above issue, model-based approaches incorporating traditional analytical prior terms have been employed, making it challenging to capture finer details of anatomical bio-structures. In this paper, we proposed an innovative prior named group sparsity prior for simultaneous reconstruction, which utilizes the non-local structural similarity between patches extracted from internal AR-PAM images. The local image details and resolution are improved while artifacts are also introduced. To mitigate the artifacts introduced by patch-based reconstruction methods, we further integrate an external image dataset as an extra information provider and consolidate the group sparsity prior with a deep denoiser prior. In this way, complementary information can be exploited to improve reconstruction results. Extensive experiments are conducted to enhance the simulated and in vivo AR-PAM imaging results. Specifically, in the simulated images, the mean peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values have increased from 16.36 dB and 0.46 to 27.62 dB and 0.92, respectively. The in vivo reconstructed results also demonstrate the proposed method achieves superior local and global perceptual qualities, the metrics of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) have significantly increased from 10.59 and 8.61 to 30.83 and 27.54, respectively. Additionally, reconstruction fidelity is validated with the optical resolution photoacoustic microscopy (OR-PAM) data as reference image.

关键词： Imaging Image reconstruction Acoustics Biomedical imaging Optical imaging Mathematical models Focusing Biomedical optical imaging Microscopy reconstruction algorithms Acoustic resolution photoacoustic microscopy deep denoiser prior group sparsity prior non-local self-similarity photoacoustic imaging

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FPGA Implementation of Staged Projection Refining Multiple Orthogonal Matching Pursuit Algorithm for Compressed Sensing

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第5期33卷 1334-1347页

作者： Liu, Sujuan Liang, Yichen Zhang, Zixing Wan, Peiyuan Beijing Univ Technol Coll Microelect Beijing 100124 Peoples R China

reconstruction algorithms are an integral part of compressed sensing (CS) theory, which can reliably reconstruct the original signal from the low-dimensional compressed signal. The orthogonal matching pursuit (OMP) algorithm has been widely studied and extensively selected in hardware implementations. However, the low reconstruction success rate of the OMP algorithm under high sparsity conditions has led to the proposal and application of more reconstruction algorithms in hardware implementations. In this article, a staged projection refining multiple OMP (SPR-MOMP) algorithm is proposed based on the OMP algorithm. This algorithm improves the reconstruction accuracy by refining the support set using a staged backtracking strategy. It also employs a multiple-atom selection strategy for parallel expansion of the support set, ensuring reconstruction efficiency. The reconstruction simulation demonstrates that the SPR-MOMP algorithm achieves a higher reconstruction success rate than the OMP algorithm, with fewer iterations. A hardware architecture applying the SPR-MOMP algorithm is designed and implemented on a Virtex UltraScale+ field-programmable gate array (FPGA) with N = 1024, M = 256, and K = 36. The proposed architecture achieves a reconstruction signal-to-noise ratio (RSNR) of 44.27 dB, with 20-bit data width and 15-bit fractional width. The maximum clock frequency of the architecture is 200 MHz, enabling reconstruction within 276.6 mu s. The proposed architecture achieves a lower dynamic power consumption of 1929 mW.

关键词： Computer architecture Matching pursuit algorithms Hardware Field programmable gate arrays Image reconstruction Heuristic algorithms Backtracking reconstruction algorithms Signal processing algorithms Sensors Compressed sensing (CS) field-programmable gate array (FPGA) hardware reconstruction orthogonal matching pursuit (OMP) QR decomposition (QRD)

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Interchannel Antenna Pattern Correction for Azimuth Digital Beamforming in Airborne SAR

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Castillo, Juan Pablo Navarro Scheiber, Rolf Jaeger, Marc Moreira, Alberto German Aerosp Ctr DLR Microwaves & Radar Inst D-82234 Wessling Germany

High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.

关键词： Antennas Azimuth Synthetic aperture radar reconstruction algorithms Receiving antennas Image reconstruction Antenna radiation patterns Filters Doppler effect Spaceborne radar Airborne synthetic aperture radar (SAR) antenna pattern azimuth SAR image reconstruction digital beamforming (DBF) range block processing

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PrideDiff: Physics-Regularized Generalized Diffusion Model for CT reconstruction

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IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2025年第2期9卷 157-168页

作者： Lu, Zexin Gao, Qi Wang, Tao Yang, Ziyuan Wang, Zhiwen Yu, Hui Chen, Hu Zhou, Jiliu Shan, Hongming Zhang, Yi Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Sichuan Univ Key Lab Data Protect & Intelligent Management Minist Educ Chengdu 610207 Peoples R China Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China Sichuan Univ Key Lab Data Protect & Intelligent Management Minist Educ Chengdu 610207 Peoples R China

Achieving a lower radiation dose and a faster imaging speed is a pivotal objective of computed tomography (CT) reconstruction. However, these often come at the cost of compromised reconstruction quality. With the advent of deep learning, numerous CT reconstruction methods rooted in this field have significantly improved the reconstruction performance. Recently, diffusion models have further enhanced training stability and imaging quality for CT. However, many of these methods only focus on CT image domain features, ignoring the intrinsic physical information of the imaging process. Although compressive sensing-based iterative reconstruction algorithms utilize physical prior information, their intricate iterative process poses challenges in training, subsequently influencing their efficiency. Motivated by these observations, we introduce a novel physics-regularized generalized diffusion model for CT reconstruction (PrideDiff). On the one hand, our method further improves the quality of reconstructed images by fusing physics-regularized iterative reconstruction methods with diffusion models. On the other hand, we propose a prior extraction module embedded with temporal features, which effectively improves the performance of the iteration process. Extensive experimental results demonstrate that PrideDiff outperforms several state-of-the-art methods in low-dose and sparse-view CT reconstruction tasks on different datasets, with faster reconstruction speed. We further discuss the effectiveness of relevant components in PrideDiff and validate the stability of the iterative reconstruction process, followed by detailed analysis of computational cost and inference time.

关键词： Image reconstruction Computed tomography Diffusion models Training Noise Feature extraction reconstruction algorithms Plasmas Image restoration Diffusion processes Deep learning (DL) diffusion models iterative reconstruction low-dose computed tomography (CT) sparse-view CT

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OPTIMAL DATA REPLICATION - A NEW APPROACH TO OPTIMIZING PARALLEL EM algorithms ON A MESH-CONNECTED MULTIPROCESSOR FOR 3D PET IMAGE-reconstruction

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1995年第4期42卷 1235-1245页

作者： CHEN, CM LEE, SY AUBURN UNIV DEPT ELECT ENGNAUBURNAL 36849

The EM algorithm promises an estimated image with the maximal likelihood for 3D PET image reconstruction. However, due to its long computation time, the EM algorithm has not been widely used in practice. While several parallel implementations of the EM algorithm have been developed to make the EM algorithm feasible, they do not guarantee an optimal parallelization efficiency. In this paper, we propose a new parallel EM algorithm which maximizes the performance by optimizing data replication on a mesh-connected message-passing multiprocessor. To optimize data replication, we have formally derived the optimal allocation of shared data, group sizes, integration and broadcasting of replicated data as well as the scheduling of shared data accesses. The proposed parallel EM algorithm has been implemented on an iPSC/860 with 16 PEs. The experimental and theoretical results, which are consistent with each other, have shown that the proposed parallel EM algorithm could improve performance substantially over those using unoptimized data replication.

关键词： Positron emission tomography Image reconstruction reconstruction algorithms Iterative algorithms Image converters Biomedical engineering Biomedical computing Broadcasting Data visualization Radioactive materials

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Spk2ImgMamba: Spiking Camera Image reconstruction with Multi-Scale State Space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jiaoyang Yin Bin Fan Chao Xu Tiejun Huang Boxin Shi State Key Lab of Multimedia Info. Processing School of Computer Science Peking University Nat'l Eng. Research Ctr. of Visual Technology School of Computer Science Peking University Nat'l Key Lab of General AI School of Intelligence Science and Technology Peking University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba.

关键词： Correlation Computational modeling reconstruction algorithms Vision sensors Transformer cores Streaming media Cameras Transformers Image reconstruction Streams

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