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Conference on Multimodal image Exploitation and Learning

作者： Pham, Kevin Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673854;9781510673847

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution's performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

关键词： image compression efficient AI real time processing

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Ultra-high spatio-temporal resolution imaging with parallel acquisition-readout structured illumination microscopy(PAR-SIM)

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Light(Science & applications) 2024年第6期13卷 1140-1158页

作者： xinzhu xu Wenyi Wang Liang Qiao Yunzhe Fu xichuan Ge Kun Zhao Karl Zhanghao Meiling Guan xin Chen Meiqi Li Dayong Jin Peng xi Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijing100871China Wallace H.Coulter Dept.of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlanta30332GAUSA National Biomedical Imaging Center Peking UniversityBeijing100871China Department of Biomedical Engineering College of EngineeringSouthern University of Science and TechnologyShenzhen518055GuangdongChina Airy Technologies Co. Ltd.Beijing100086China Eastern Institute for Advanced Study Eastern Institute of TechnologyNingboZhejiang315200China School of Life Science Peking UniversityBeijing100871China Institute for Biomedical Materials and Devices(IBMD) Faculty of ScienceUniversity of Technology SydneySydneyNSW2007Australia

Structured illumination microscopy(SIM)has emerged as a promising super-resolution fluorescence imaging technique,offering diverse configurations and computational strategies to mitigate phototoxicity during real-time imaging of biological *** efforts to enhance system frame rates have concentrated on processing algorithms,like rolling reconstruction or reduced frame reconstruction,or on investments in costly sCMOS cameras with accelerated row readout *** this article,we introduce an approach to elevate SIM frame rates and region of interest(ROI)coverage at the hardware level,without necessitating an upsurge in camera expenses or intricate ***,parallel acquisition-readout SIM(PAR-SIM)achieves the highest imaging speed for fluorescence imaging at currently available detector *** using the full frame-width of the detector through synchronizing the pattern generation and image exposure-readout process,we have achieved a fundamentally stupendous information spatial-temporal flux of 132.9 MPixels·s^(−1),9.6-fold that of the latest techniques,with the lowest SNR of−2.11 dB and 100 nm ***-SIM demonstrates its proficiency in successfully reconstructing diverse cellular organelles in dual excitations,even under conditions of low signal due to ultra-short exposure ***,mitochondrial dynamic tubulation and ongoing membrane fusion processes have been captured in live COS-7 cell,recorded with PAR-SIM at an impressive 408 *** posit that this novel parallel exposure-readout mode not only augments SIM pattern modulation for superior frame rates but also holds the potential to benefit other complex imaging systems with a strategic controlling approach.

关键词： hardware illumination resolution

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An Overview of Neutrosophic Graphs

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Neutrosophic Sets and systems 2025年 77卷 450-462页

作者： Panda, Nihar Ranjan Raut, Prasanta Kumar Baral, Amarendra Sahoo, Srikanta Kumar Satapathy, Sakya Singh Broumi, Said IMS & SUM Hospital Siksha o Anusandhan Deemed to be University India Department of Mathematics Trident Academy of Technology Odisha Bhubaneswar India National Institute of Technology Agartala India Laboratory of Information Processing Faculty of Science Ben M’Sik University Hassan II Casablanca Morocco

A new area of study called "neutrosophic graph theory" uses neutrosophic logic to expand on traditional graph theory to include ambiguous, indeterminate, and uncertain information. This paper gives a thorough introduction to neutrosophic graphs, including their definition, components, uses, and difficulties. Neutrosophic graphs let you show and analyze relationships that aren't clear-cut because they give vertices and edges different levels of truth, indeterminacy, and falsity. image processing, social network analysis, pattern recognition, and decision-making in the face of uncertainty all use neutrosophic graphs. However, we still need to resolve issues like interpretability and computational complexity. All things considered, neutrosophic graphs provide a viable framework for representing and evaluating ambiguous information in graph structures, with potential applications in a variety of fields. © 2025, University of New Mexico. All rights reserved.

关键词： Graph algorithms

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A Real-Time 2D/3D Perception Visual Vector Processor for 1920 x 1080 High-Resolution High-Speed Intelligent Vision Chips

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IEEE TRANSACTIONS ON CIRCUITS AND systems I-REGULAR PAPERS 2024年第2期71卷 740-753页

作者： Wei, Siyuan Ning, Ke Kang, Lei Zheng, xuemin Zhao, Mingxin xu, Mengmeng Wang, Shuyu xu, xuanzhe Dou, Runjiang Yu, Shuangming Yang, xu Liu, Jian Shi, Cong Wu, Nanjian Liu, Liyuan Chinese Acad Sci Inst Semicond State Key Lab Superlatt & Microstruct Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol State Key Lab Superlatt & Microstruct Beijing 100083 Peoples R China Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China

Edge computing of reliable multimodal (2D RGB/3D RGB-Depth) data has a wide range of applications. However, many of currently reported visual processors cannot flexibly handle multimodal data, e.g., the visual streams of RGB-Depth data. The key challenge exists that these prior visual processors do not come with efficient and unified instruction set architecture (ISA) for both conventional and intelligent cognition on the 2D/3D multimodal sensory data. To fill such a gap, this paper proposes a programmable intelligent visual vector processor compatible with multimodal 2D/3D visual data processing ( $1920\times 1080$ -pixel resolution). The processor consists of a reconfigurable processing element (PE) array, a memory access network flexibly configurable to be fine-or coarse-grained, and a high throughput I/O interface. The vectorial PE array with neighbor PE access increases the data reuse rate and parallel computation efficiency, and can implement both convolutional neural networks (CNNs) and conventional image processing algorithms. The proposed ISA is customized and optimally tailored targeting 2D/3D image processing from RGB/Time-of-Flight(ToF) raw data to intelligent inference results. The chip is fabricated in a 55-nm CMOS process. The experimental results showed that the area efficiency, peak performance, and peak throughput of our chip attained as high as 14.41 GOPS/mm2, 409.6 GOPS, and 9.6 Gbps at 200 MHz, respectively. The measured processing speeds of this chip on ToF depth reconstruction is 87 fps (480 x 270) or 31 fps(1920 x 1080),on 3D object classification is 219 fps (256 x 256), and on CNN-based 2D object tracking is 36 fps (256 x 256).

关键词： Visual processor near-sensor computing parallel computation 2D/3D image processing vision chip

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Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 229卷

作者： Paul, Nijhum Sunil, G. C. Horvath, David Sun, xin North Dakota State Univ Dept Agr & Biosyst Engn Fargo ND 58108 USA North Dakota State Univ Genom Phen & Bioinformat Program Fargo ND 58108 USA USDA ARS ETSARC Weed & insect Biol unit Fargo ND 58102 USA

Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a thorough overview of the state-of-the-art DL technologies for plant stress detection. For this purpose, a systematic literature review was conducted to identify relevant articles for highlighting the technologies and approaches currently employed in the development of a DL-based plant stress detection system, specifically the advancement of image-based data collection systems, image preprocessing techniques, and deep learning algorithms and their applications in plant stress classification, disease detection, and segmentation tasks. Additionally, this review emphasizes the challenges and future directions in collecting and preprocessing image data, model development, and deployment in real-world agricultural settings. Some of the key findings from this review paper are: Training data: (i) Most plant stress detection models have been trained on Red Green Blue (RGB) images;(ii) Data augmentation can increase both the quantity and variation of training data;(iii) Handling multimodal inputs (e. g., image, temperature, humidity) allows the model to leverage information from diverse sources, which can improve prediction accuracy;Model Design and Efficiency: (i) Self-supervised learning (SSL) and Few-shot learning (FSL)-based methods may be better than transfer learning (TL)-based models for classifying plant stress when the number of labeled training images are scarce;(ii) Custom designed DL architectures for a specific stress and plant type can have better performance than the state-of-the-art DL architectures in terms of efficiency, overfitting, and accuracy;(iii) The multi-task learning DL structure reuses most of the network architecture while performing multiple tasks (e.g., estimate stress type and severity) simultaneously, which makes the learning much

关键词： Plant stress Computer vision Deep learning image processing imaging sensor Precision agriculture

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DBMamba-UNet: dual-branch mamba in vision for 3D hippocampus image segmentation

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JOURNAL OF ELECTRONIC imaging 2025年第2期34卷

作者： Yu, Lei Wang, Zunkai Chongqing Normal Univ Sch Comp Sci & Informat Technol Chongqing Peoples R China

Hippocampal segmentation is a crucial task in medical image processing, playing a significant role in the diagnosis and research of neurodegenerative diseases such as Alzheimer's disease. The hippocampus, located in the medial temporal lobe of the human brain, is a bilayer gray matter structure that protrudes into the temporal horn of the lateral ventricle and occupies its medial base. Due to its small size, irregular shape, and indistinct boundaries, accurate segmentation of the hippocampus poses considerable challenges. Currently, most hippocampal segmentation methods rely on magnetic resonance imaging (MRI). However, using structural MRI alone often fails to achieve precise segmentation. To enhance segmentation accuracy, researchers have adopted various approaches. Among these, deep learning-based methods have gained significant attention for their powerful feature extraction and classification capabilities. Nevertheless, these methods tend to confuse edge regions during segmentation, reducing overall accuracy. Transformer-based models, known for capturing long-range dependencies effectively, have also been explored, but their extensive parameter requirements often limit practical applications in segmentation tasks. To address these challenges, we propose a dual-branch Mamba-inspired U-Net (DBMamba-UNet). The dual-branch structure preserves rich detail information, particularly edge features, at high resolutions. Features of different scales from the main and auxiliary branches are fused using the FMamba module before being passed to the upsampling layer. The Mamba-inspired visual architecture, utilized as both encoder and decoder, combines the advantages of transformers and Mamba structures. This design reduces the computational resources required for segmentation tasks while enabling parallel processing. Compared with state-of-the-art algorithms, DBMamba-UNet achieves superior segmentation performance with significantly lower computational costs, delivering bet

关键词： multifeature fusion mamba asymmetric U-Net linear attention semantic segmentation

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Performance Study of an MRI Motion-Compensated Reconstruction Program on Intel CPUs, AMD EPYC CPUs, and NVIDIA GPUs

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APPLIED SCIENCES-BASEL 2024年第21期14卷 9663页

作者： Zeroual, Mohamed Aziz Isaieva, Karyna Vuissoz, Pierre-Andre Odille, Freddy Inserm IADI U1254 F-54000 Nancy France Univ Lorraine F-54000 Nancy France Univ Lorraine CIC IT 1433 Inserm F-54000 Nancy France CHRU Nancy F-54000 Nancy France

Motion-compensated image reconstruction enables new clinical applications of Magnetic Resonance imaging (MRI), but it relies on computationally intensive algorithms. This study focuses on the Generalized Reconstruction by Inversion of Coupled systems (GRICS) program, applied to the reconstruction of 3D images in cases of non-rigid or rigid motion. It uses hybrid parallelization with the MPI (Message Passing Interface) and OpenMP (Open Multi-processing). For clinical integration, the GRICS needs to efficiently harness the computational resources of compute nodes. We aim to improve the GRICS's performance without any code modification. This work presents a performance study of GRICS on two CPU architectures: Intel xeon Gold and AMD EPYC. The roofline model is used to study the software-hardware interaction and quantify the code's performance. For CPU-GPU comparison purposes, we propose a preliminary MATLAB-GPU implementation of the GRICS's reconstruction kernel. We establish the roofline model of the kernel on two NVIDIA GPU architectures: Quadro RTx 5000 and A100. After the performance study, we propose some optimization patterns for the code's execution on CPUs, first considering only the OpenMP implementation using thread binding and affinity and appropriate architecture-compilation flags and then looking for the optimal combination of MPI processes and OpenMP threads in the case of the hybrid MPI-OpenMP implementation. The results show that the GRICS performed well on the AMD EPYC CPUs, with an architectural efficiency of 52%. The kernel's execution was fast on the NVIDIA A100 GPU, but the roofline model reported low architectural efficiency and utilization.

关键词： magnetic resonance imaging reconstruction motion high-performance computing parallel processing

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Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep Clustering Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 284-296页

作者： Shahi, Kasra Rafiezadeh Ghamisi, Pedram Rasti, Behnood Scheunders, Paul Gloaguen, Richard Helmholtz Zentrum Dresden Rossendorf Helmholtz Inst Freiberg Resource Technol D-09599 Freiberg Germany Univ Antwerp Dept Phys Imec Visionlab B-2000 Antwerp Belgium

The ever-growing developments in technology to capture different types of image data [e.g., hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface model (DSM)], along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multisensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at [Online]. Available: https://***/Kasra2020/MDC.

关键词： Clustering algorithms image reconstruction Feature extraction Decoding Spatial resolution imaging Hyperspectral imaging Autoencoder (AE) convolutional autoencoder (CAE) deep learning multisensor data fusion remote sensing

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All-optical geometric image transformations enabled by ultrathin metasurfaces

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NATURE COMMUNICATIONS 2023年第1期14卷 1-8页

作者： Zhang, xingwang Zhang, xiaojie Duan, Yao Zhang, Lidan Ni, xingjie Penn State Univ Dept Elect Engn University Pk PA 16802 USA

image processing plays a vital role in artificial visual systems, which have diverse applications in areas such as biomedical imaging and machine vision. In particular, optical analog image processing is of great interest because of its parallel processing capability and low power consumption. Here, we present ultra-compact metasurfaces performing all-optical geometric image transformations, which are essential for image processing to correct image distortions, create special image effects, and morph one image into another. We show that our metasurfaces can realize binary image transformations by modifying the spatial relationship between pixels and converting binary images from Cartesian to log-polar coordinates with unparalleled advantages for scale- and rotation-invariant image preprocessing. Furthermore, we extend our approach to grayscale image transformations and convert an image with Gaussian intensity profile into another image with flat-top intensity profile. Our technique will potentially unlock new opportunities for various applications such as target tracking and laser manufacturing. Metasurfaces enable all-optical geometric coordinate transformations, converting images with altered pixel spatial relations, which can facilitate fast, energy-efficient preprocessing for tasks like object tracking, or aid in laser manufacturing.

关键词： Metamaterials Nanophotonics and plasmonics

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Multiband remote 3D-ViT data fusion 30

Multiband remote 3D-ViT data fusion

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Conference on algorithms, Technologies, and applications for Multispectral and Hyperspectral imaging xxx

作者： Kurz, Aidan G. Depoian, Arthur C., ii Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673816;9781510673809

Satellites are equipped with diverse sensors, capable of capturing detailed information across a multitude of wavelengths. The fusion of multispectral data is pivotal to amplify the visual representation of the area of interest. The improvement of information representation allows for enhanced processing, analysis, and other crucial tasks for numerous fields of study, including remote sensing, defense, and material characterization. Previous solutions often utilize traditional signal processing techniques, including principal component analysis (PCA), to accomplish data fusion. By performing fusion on a feature level, extracted information about the area of interest texture and boundaries are combined. The introduction of neural network techniques improved the reconstruction of data similar to the results obtained by conventional inference of humans. For example, the use of deep learning algorithms in conjunction with PCA allowed for refined reduction of redundancy and distortion of spectral data, in comparison to traditional methods alone. The introduction of the Vision Transformer (ViT) architecture, originally developed for two-dimensional image data, has revolutionized image processing tasks, vastly improving performance at the cost of a large quantity of trainable parameters. Recent experimentation has proven that optimizing ViT for efficiency allows for comparable or even superior performance while lessening the computational cost. The transition from 2D to 3D information via utilization of additional depth and spatial data has also led to superior results as the added information allows for better representation of terrain features, making it invaluable for satellite imagery analysis. Combining the principles of ViT and 3D information to process complex satellite data can result in more effective data fusion to achieve a superior level of data visualization of multispectral satellite imagery in an efficient manner.

关键词： Data Fusion Multispectral Fusion Remote Sensing Deep Learning

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