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Broadband and parallel multiple-order optical spatial differentiation enabled by Bessel vortex modulated metalens

NATURE COMMUNICATIONS 2024年第1期15卷 9045页

作者： Huo, Pengcheng Tan, Le Jin, Yaqi Zhang, Yanzeng Liu, Mingze Lin, Peicheng Zhang, Song Wang, Yilin Ren, Haiyang Lu, Yanqing Xu, Ting Nanjing Univ Coll Engn & Appl Sci Collaborat Innovat Ctr Adv Microstruct Natl Lab Solid State Microstruct Nanjing 210093 Peoples R China Nanjing Univ Key Lab Intelligent Opt Sensing & Manipulat Nanjing 210093 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210093 Peoples R China Jiangsu Univ Technol Sch Mat Engn Changzhou 213001 Peoples R China

Optical analog image processing technology is expected to provide an effective solution for high-throughput and real-time data processing with low power consumption. In various operations, optical spatial differential operations are essential in edge extraction, data compression, and feature classification. Unfortunately, existing methods can only perform low-order or selectively perform a particular high-order differential operation. Here, we propose and experimentally demonstrate a Bessel vortex modulated metalens composed of a single complex amplitude metasurface, which can perform multiple-order radial differential operations over a wide band by presetting the order of the corresponding Bessel vortex. This architecture further enables angle multiplexing to create multiple information channels that synchronously perform multi-order spatial differential operations, indicating the superiority of the proposed devices in parallel processing. Our approach may find various applications in artificial intelligence, machine vision, autonomous driving, and advanced biomedical imaging. A Bessel vortex modulated metalens is used for wide-band, multiple-order radial differential operations by presetting the order of the corresponding Bessel vortex. This enables synchronous spatial differential operations, with applications in optical data processing, machine vision and bio-imaging.

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Polarimetric imaging mobile phone application for large-area surface inspection 27

Polarimetric imaging mobile phone application for large-area...

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Conference on Novel Optical Systems, Methods, and applications XXVII

作者： Martini, Carlo Hahlweg, Cornelius F. Bbw Hsch Wagner Regeny Str 21 D-12489 Berlin Germany

ISBN: (纸本)9781510679207;9781510679214

This paper is intended as a short follow-up to our last publication on the use of polarization-based imaging techniques and metrology applications. With their increasing resolution, memory availability and computing capacity, smartphones have become a common means for image and video documentation, geodetic and metrological data acquisition and similar applications in recent years. We are investigating concepts for smartphone-based polarimetric image acquisition and evaluation in field measurement campaigns. Technical applications are, for example, the mobile examination of larger areas with regard to contamination, damage to coatings, corrosion and the like, but also artistic photography, e.g. for paintings and sculptures. In addition to pure data acquisition, image pre-processing and analysis can be carried out in the field by means of mobile image processing applications. In the paper we discuss such concepts, their feasibility and robust extensions for smartphones that could lead to applications that actually take advantage of the smartphone's mobility.

关键词： polarization imaging mobile device surface inspection contamination detection dielectric layers cross- and parallel polarization

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parallel VLSI Architectures for High-Throughput Image processing in Medical imaging 5

Parallel VLSI Architectures for High-Throughput Image Proces...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Vinayagam, P. Yuvaraaj, R. Sathiyanandham, S. Kanagamalliga, S. Saveetha Engineering College Chennai602105 India

The need for efficient and reliable image processing techniques has never been greater in medical imaging. The proposed work analyzes and designs parallel VLSI (Very Large-Scale Integration) architectures that meet the stringent demands of high-throughput medical image processing. The remainder of the paper is organized as follows, beginning with a discussion of the computational challenges that arise organically in medical imaging and why it is crucial to surmount these obstacles. The section on the proposed work describes the architecture, parallelization strategies, and processing pipeline, providing context for the hardware and Software underlying the system. The evaluation results below demonstrate significant enhancements in processing speed and image quality. Due to their real-time capabilities and high efficiency, parallel VLSI architectures have the potential to revolutionize medical image processing, resulting in more accurate diagnoses and more effective treatments. Research into optimizing these structures and discovering new applications is anticipated to result in further advances in medical imaging. © 2024 The Authors. Published by Elsevier B.V.

关键词： Medical imaging

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Algorithms to Speed up Contour Tracing in Real Time Image processing Systems

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IEEE ACCESS 2022年 10卷 127365-127376页

作者： Gupta, Sonal Kar, Subrat Indian Inst Technol Delhi IIT Delhi Dept Elect Engn New Delhi 110016 India

Contour tracing is an important pre-processing step in many image-processing applications such as feature recognition, biomedical imaging, security and surveillance. As single-processor architectures reach their performance limits, parallel processing architectures offer energy-efficient and high-performance solutions for real-time applications. parallel processing architectures are thus used for several real-time image processing applications. Among the several interconnection schemes available, Cayley graph-based interconnections offer easy routing and symmetric implementation capabilities. For parallel processing systems with a Cayley graph-based interconnection scheme, torus, we developed three accelerated algorithms corresponding to three existing families of contour tracing algorithms. We simulated these algorithms on a parallel processing framework to quantify the normalized speed-up possible in any torus-connected parallel processing system. We also compared our best-performing algorithm with the existing parallel processing implementations for Nvidia GPUs. We observed a speed-up of up to 468 times using our algorithms on a parallel processing architecture in comparison to the corresponding algorithm on a single processor architecture. We evaluated a speedup of 194 (and 47) compared to the existing parallel processing contour tracing implementation on Tesla K40c (and Quadro RTX 5000 GPU hardware, respectively). We observe that for torus-connected parallel processing architectures used for image processing, our algorithms can speed up contour tracing without any hardware modification.

关键词： parallel processing Computer architecture Hardware Graphics processing units Image processing Random access memory Image segmentation Accelerated contour tracing image processing parallel algorithms multiprocessors torus GPU

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Hybrid Spatial and Temporal Computing Histogramer in Soft Processor Core of a FPGA Device

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第8期71卷 3683-3694页

作者： Ronconi, Enrico Garzetti, Fabio Lusardi, Nicola Costa, Andrea Geraci, Angelo Politecn Milan Dept Elect Informat & Bioengn DEIB I-20133 Milan Italy

Multi-channel data management is crucial in a world where big data processing is extensively used in research and business. Histogramming is a common technique employed to detect, analyze, and store enormous volumes of data in real-time, making it useful for industrial applications in fields such as biology, chemistry, medical imaging, and spectroscopy. Due for them programming simplicity and low-cost large amount of memory, general-purpose temporal computing processors are commonly used, but they lack the ability to perform parallel computation at high-performance. Field-Programmable Gate Array (FPGA) is a powerful parallel computing solution proposed by both the scientific and industrial worlds, but it is equipped with little memory for these applications. Thus, a hybrid spatial/temporal computing histogram generator has been proposed, which uses a low-area multi-channel histogramming engine in programmable logic which is expanded thanks to an external Double Data Rate Synchronous Dynamic Random Access Memory (DDR) driven by a MicroBlaze Soft Processor Core. The proposed system has been validated on a Xilinx 28-nm 7-Series Artix-7 XC7A100T FPGA hosted on a Nexys4 Evaluation Board. Thanks to this hybrid solution, up to 128 channels can handle in a low-end FPGA occupies 207 LUTs and 325 flip-flops per channel plus a total 630 kb of total BRAM shared between all channels;a power consumption of 10.1 mW per channel is measured.

关键词： Histograms Field programmable gate arrays Random access memory Real-time systems parallel processing Hardware Data acquisition field-programmable gate array (FPGA) MicroBlaze real-time systems soft processor core

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Real-time diabetic foot ulcer classification based on deep learning & parallel hardware computational tools

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MULTIMEDIA TOOLS AND applications 2024年第27期83卷 70369-70394页

作者： Fadhel, Mohammed A. Alzubaidi, Laith Gu, Yuantong Santamaria, Jose Duan, Ye Univ Sumer Coll Comp Sci & Informat Technol Thi Qar 64005 Iraq Queensland Univ Technol Sch Mech Med & Proc Engn Brisbane Qld 4000 Australia Queensland Univ Technol ARC Ind Transformat Training Ctr Joint Biomech Brisbane Qld 4000 Australia Akunah Med Technol Pty Ltd Co Brisbane Qld 4120 Australia Univ Jaen Dept Comp Sci Jaen 23071 Spain Clemson Univ Sch Comp Clemson SC 29631 USA

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer-DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs.

关键词： Deep learning Double transfer learning FPGA GPU DFU Real-time parallel hardware Medical imaging

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Joint loss function design in diffractive optical neural network classifiers for high power efficiency

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Optics Express 2025年第4期33卷 7307-7320页

作者： Fan, Mengguang Jin, Shuping Gu, Yinwei Zhao, Xiangyue Bai, Ningfeng Wang, Qilong Lu, Changgui School of Electronic Science and Engineering Southeast University Nanjing211189 China

The high speed, wide bandwidth, and parallel processing capabilities of a diffractive optical neural network (DONN) stimulate its applications in computer vision for image recognition and information processing tasks. This paper presents a joint loss function (J-SCE) that combines classification performance and diffractive power efficiency, thereby improving the power efficiency of the DONN classifier from 0.92% to 12.89% while maintaining a classification accuracy of 95.36%. The J-SCE function improves the overall power efficiency of the system by directing energy more effectively toward the target area. Furthermore, the J-SCE function enhances the system’s robustness to noise and overall stability. This work significantly contributes to the application of DONN classifiers in practical image recognition and other information processing scenarios. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： imaging systems Information processing Light matter interactions Machine vision Optical computing Optical neural systems

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An adaptive multi-frame parallel iterative method for accelerating real-time magnetic particle imaging reconstruction

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PHYSICS IN MEDICINE AND BIOLOGY 2023年第24期68卷 245016-245016页

作者： Shen, Yusong Zhang, Liwen Shang, Yaxin Jia, Guang Yin, Lin Zhang, Hui Tian, Jie Yang, Guanyu Hui, Hui Southeast Univ Sch Comp Sci & Engn Nanjing Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging Beijing Peoples R China Beijing Key Lab Mol Imaging Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100080 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China Beihang Univ Sch Engn Med Beijing Peoples R China Minist Ind & Informat Technol Peoples Republ China Key Lab Big Data Based Precis Med Beijing Peoples R China

Objective. Real-time reconstruction of magnetic particle imaging (MPI) shows promising clinical applications. However, prevalent reconstruction methods are mainly based on serial iteration, which causes large delay in real-time reconstruction. In order to achieve lower latency in real-time MPI reconstruction, we propose a parallel method for accelerating the speed of reconstruction methods. Approach. The proposed method, named adaptive multi-frame parallel iterative method (AMPIM), enables the processing of multi-frame signals to multi-frame MPI images in parallel. To facilitate parallel computing, we further propose an acceleration strategy for parallel computation to improve the computational efficiency of our AMPIM. Main results. OpenMPIData was used to evaluate our AMPIM, and the results show that our AMPIM improves the reconstruction frame rate per second of real-time MPI reconstruction by two orders of magnitude compared to prevalent iterative algorithms including the Kaczmarz algorithm, the conjugate gradient normal residual algorithm, and the alternating direction method of multipliers algorithm. The reconstructed image using AMPIM has high contrast-to-noise with reducing artifacts. Significance. The AMPIM can parallelly optimize least squares problems with multiple right-hand sides by exploiting the dimension of the right-hand side. AMPIM has great potential for application in real-time MPI imaging with high imaging frame rate.

关键词： magnetic particle imaging real-time reconstruction parallel iterative method parallel computation

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Parametric Pipelined k-Means Implementation for Hyperspectral processing on Spacecraft Embedded FPGA

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15927-15941页

作者： Morcillo, Borja Bascones, Daniel Gonzalez, Carlos Mendias, Jose M. Mozos, Daniel Univ Complutense Madrid Dept Comp Architecture & Automat Madrid 28035 Spain

k-means stands out as one of the most common clustering algorithms, widely employed for classification in hyperspectral imaging. In this context, large amounts of data are gathered by sensors that are embedded into satellites with strict constraints in terms of power consumption, weight, physical space or radiation tolerance. Since communication bandwidth is also limited, data processing must be performed on board. However, meeting all those constraints also entails a significant tradeoff with computing performance. The aim of this work is clustering hyperspectral images in real time. Custom hardware has been designed with the objective of reducing overhead and maximizing performance, by exploiting several acceleration techniques. The implementation targets a space-grade Xilinx Kintex FPGA, which features low power consumption and is shielded against radiation. The design has a deep pipelined architecture, able to process all bands of each hyperspectral pixel in parallel. In consequence, it attains a throughput of 100 M hyperspectral pixels per second, even with a discrete use of FPGA resources. In addition, it is also fully parametric, with on-the-fly adaptation to different kinds of images and clustering configurations. Compared to previous implementations, ours takes advantage of a fully RTL design that avoids CPU bottlenecks and HLS design overheads. It also has a fixed throughput regardless of image or clustering properties, while having lower FPGA resource usage than performancewise equivalent implementations.

关键词： Hyperspectral imaging Field programmable gate arrays Hardware Clustering algorithms Power demand Performance evaluation Satellites Clustering methods multidimensional signal processing reconfigurable architectures satellite applications

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Color Volumetric imaging With High Dynamic Range Based on parallel Confocal Detection

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IEEE PHOTONICS TECHNOLOGY LETTERS 2024年第14期36卷 881-884页

作者： Lee, Woojin Kim, Chang-Soo Lee, Dong-Ryoung Yoo, Hongki Korea Adv Inst Sci & Technol Dept Mech Engn Daejeon 34141 South Korea Nexensor Inc Daejeon 34047 South Korea Soongsil Univ Sch Mech Engn Seoul 06978 South Korea

Reflectance confocal microscopy is widely used in several applications that require non-destructive optical three-dimensional (3D) imaging. However, the acquisition speed of conventional confocal microscopy is limited by mechanical 3D scanning. Furthermore, acquiring data from a sample with excessively different degrees of reflectance is difficult. We propose a system that combines the high dynamic range (HDR) technique and parallelized confocal detection to achieve high-speed volumetric imaging for samples with varying reflectivity. Our technique sequentially acquires two-dimensional color images by projecting patterns onto the objects and using a focus tunable lens for axially scanning, thereby creating a volumetric profile. By modulating the power of the light source in synchronization with the axial scanning direction, two volumetric profiles are produced and fused them to reconstruct an HDR volumetric profile. We demonstrate that the proposed technique can capture thorough 3D profiles of samples even with a wide range of reflectivity.

关键词： Three-dimensional displays Image color analysis imaging Lenses Reflectivity Standards Light emitting diodes Microscopy image processing high dynamic range volume measurement

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