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8th International Conference on Multimedia and image processing, ICMIP 2023

作者： Mei, Jianqiang Jia, Fan Chen, Wanyan Li, Biyuan School of Electronic Engineering Tianjin University of Technology and Education Tianjin China Raysov Instrument Co. Ltd Dandong China

ISBN: (纸本)9781450399586

imaging quality has always been the most critical issue during the analog to digital development of the x-ray non-destructive testing (NDT). Due to the complicated structure of evaluated objects, it is still difficult for human eyes to clearly distinguish foreground objects from the background within the x-ray NDT image. Meanwhile, noise signals of the radiation imaging equipment also causes several shortcomings, such as blurring of key features. Therefore, without upgrading the hardware of industrial inspection systems, there is a strong demand to improve the quality of x-ray NDT images, particularly on content and edges. In this paper, we proposed a novel framework, termed as dual enhancement effectiveness plus sharpening (DEEPS), for quality improvement of x-ray NDT image. Specifically, an alternative implementation of multi-scale retinex with color restoration (MSRCR) is firstly employed to extract and enhance the essential information of the image. Then the combination of contrast limited adaptive histogram equalization (CLAHE) and gamma mapping is sequentially implemented to enhance the contrast and lighting. The output of our proposed framework is finally derived after an unsharp mask (USM) loop. Qualitative and quantitative comparison against the classic methods with Peak Signal-To-Noise Ratio (PSNR), Entropy and Contrast to Improve Index (Cii) demonstrate the effectiveness of our proposed framework that effectively improve image quality while preserve the edge information without introducing artefact. In addition, after parallel optimization, our method can be further utilized for real time applications of x-ray non-destructive testing scenarios. © 2023 ACM.

关键词： image enhancement

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Intelligent image processing based on VPx architecture 8

Intelligent image processing based on VPX architecture

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8th Symposium on Novel Photoelectronic Detection Technology and applications

作者： Tao, xin Ou, Weilin He, Wenbo Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Wuhan430074 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Multiple bands cameras with high pixel resolution and fast frame rate bring challenges for real-time signal process which is important in monitoring and alerting application. Especially, the deep neural network algorithm which has been applied in practical EO system, requires high parallel computation ability. Thus, the traditional signal processing method in imaging sensor cannot satisfy the novel demands for balancing the capability and consumption. The system based on VPx architecture and all optical signal transmission can provide a promising improvement in computation capability, transmission bandwidth and data transfer capability. In this paper, a novel signal processing system for intelligent computing based on VPx architecture is indicated. Based on the system structure and components indication, the main configuration design is also explained. Especially the thermal deisgn of the full system is explained in details, because the thermal effect and related methods are important in high speed conputation system. The main intelligent signal processing circuit is based on FPGA SOC processor. After introducing the hardware design and scheme, an optimized SSD algorithm is introduced for multi-scales target detection, and the method to applying these algorithms on FPGA chip for real-time compuation is explained. Finally, this method is verified in both visible and IR images, especially for the small target in IR images. The whole design is verified that it can provide a promising solution for high performance and distributed EO imaging system. © 2022 SPIE

关键词： Data transfer

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FRM-CIM: Full-Digital Recursive MAC Computing in Memory System Based on MRAM for Neural Network applications 24

FRM-CIM: Full-Digital Recursive MAC Computing in Memory Syst...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Wang, Jinkai Wang, Zekun Zhang, Bojun Gu, Zhengkun Chen, Youxiang Zhao, Weisheng Zhang, Yue Fert Beijing Institute Miit Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798400706011

Computing in memory (CIM) realizes energy-efficient neural network algorithms by implementing highly parallel multiply-and-accumulate (MAC) operation. However, the MAC delay of CIM will sharply increase with the improvement of computing precision, which restricts its development. In this work, we propose a full-digital recursive MAC (FRM) operation based on spin-transfer-torque magnetic random access memory (STT-MRAM) CIM system to enable fast and energy-efficient image recognition application. First, the fast FRM scheme is proposed by utilizing the recursive operations of read and addition in segmented bit-line array, which effectively reduces the delay of MAC operations to 3.5ns and 4ns for 8-bit and 16-bit input and weight precision, respectively. Second, we design an image recognition system using FRM-CIM architecture as the processing element (PE), where the adaptive pruning method for layers is proposed to improve the compatibility of it with the neural network. By performing image recognition for the MNIST and CIFAR-10 datasets, results show that the throughput and energy efficiency of the FRM-CIM system are 58.51TOPS/mm2 and 11.3 - 56.72 TOPS/W under 8 - 16-bit precision, which are improved by 4.3 times and 2.6 times compared with the state-of-the-art works. Finally, the recognition accuracy can reach 96.65% and 82.7% on MNIST and CIFAR-10, respectively. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Magnetic logic devices

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Measurement Accuracy and Improvement of Thematic Information from Unmanned Aerial System Sensor Products in Cultural Heritage applications

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JOURNAL OF imaging 2024年第2期10卷 34-34页

作者： Kaimaris, Dimitris Aristotle Univ Thessaloniki Sch Spatial Planning & Dev Thessaloniki 54124 Greece

In the context of producing a digital surface model (DSM) and an orthophotomosaic of a study area, a modern Unmanned Aerial System (UAS) allows us to reduce the time required both for primary data collection in the field and for data processing in the office. It features sophisticated sensors and systems, is easy to use and its products come with excellent horizontal and vertical accuracy. In this study, the UAS WingtraOne GEN ii with RGB sensor (42 Mpixel), multispectral (MS) sensor (1.2 Mpixel) and built-in multi-frequency PPK GNSS antenna (for the high accuracy calculation of the coordinates of the centers of the received images) is used. The first objective is to test and compare the accuracy of the DSMs and orthophotomosaics generated from the UAS RGB sensor images when image processing is performed using only the PPK system measurements (without Ground Control Points (GCPs)), or when processing is performed using only GCPs. For this purpose, 20 GCPs and 20 Check Points (CPs) were measured in the field. The results show that the horizontal accuracy of orthophotomosaics is similar in both processing cases. The vertical accuracy is better in the case of image processing using only the GCPs, but that is subject to change, as the survey was only conducted at one location. The second objective is to perform image fusion using the images of the above two UAS sensors and to control the spectral information transferred from the MS to the fused images. The study was carried out at three archaeological sites (Northern Greece). The combined study of the correlation matrix and the ERGAS index value at each location reveals that the process of improving the spatial resolution of MS orthophotomosaics leads to suitable fused images for classification, and therefore image fusion can be performed by utilizing the images from the two sensors.

关键词： Unmanned Aerial System digital surface model orthophotomosaic accuracy image fusion correlation table ERGAS index spectral deviation image quality

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Reconfigurable HW-SW Co-design Platform for Lung Cancer Detection and Classification 34

Reconfigurable HW-SW Co-design Platform for Lung Cancer Dete...

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34th International Conference on VLSI Design / 20th International Conference on Embedded systems (VLSID)

作者： Sharma, Ayushparth Lata, Kusum LNM Inst Informat Technol Jaipur Rajasthan India

ISBN: (纸本)9781665440875

High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. This article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. The proposed platform has been implemented on xilinx Spartan-6 FPGA board and co-simulated with xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10x higher energy-efficiency compared to serial counterpart and reduced execution period by 70x. Clinical validation shows that parallel architecture introduces 5-7% error in nodule characteristic determination in comparison to serial one.

关键词： lung cancer detection delay structures medical image processing energy-efficient platform reconfigurability

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Enhancing Medical image Security with FPGA-Accelerated LED Cryptography and LSB Watermarking

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TRAITEMENT DU SIGNAL 2024年第1期41卷 85-97页

作者： Elhamzi, Wajdi Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Engn Al Kharj 11942 Saudi Arabia Univ Sousse Higher Sch Sci & Technol Hammam Sousse Dept Comp Engn Hammam Sousse 4011 Tunisia

In telemedicine, the safeguarding of medical images is important, necessitating systems that uphold patient privacy, ensure image integrity, and verify authenticity. Addressing the challenge of processing time disparities in existing algorithms, this study introduces a novel field -programmable gate array (FPGA)-based crypto-watermarking system for medical image applications. The system integrates a least significant bit (LSB) watermarking technique with the Lightweight Encryption Device (LED) cryptography algorithm. The LSB technique, known for its minimal impact on image quality, is utilized to embed a concealed message, subsequently encrypted by the LED algorithm for enhanced security. Traditional software implementations of such algorithms have been hampered by significant processing delays, with times ranging up to 34 seconds for smaller images and extending to 30 minutes for larger ones. The predominant factor in these delays, the encryption/decryption process, occupies 98% of the total processing time. To address this, the LED algorithm has been accelerated using Vitis High -Level Synthesis (HLS) for hardware implementation, effectively reducing time to market. The proposed architecture, subjected to rigorous examination, testing, and evaluation, demonstrates superior performance in throughput and processing speed compared to previous works. An extensive range of digital images was employed to assess the coprocessor's efficacy. The results reveal an average Peak Signal -toNoise Ratio (PSNR) of 86.98 dB, indicating superior imperceptibility without attacks when compared to earlier studies. Furthermore, under various attack scenarios, the system maintains high imperceptibility, with an average PSNR of 53.68 dB, surpassing previous methods in robustness. Comparative tests confirm that the proposed FPGA-based crypto watermarking outstrips Real -Time Logic (RTL) implementations, achieving a PSNR above 82 dB. This indicates a marked improvement in imperceptibility

关键词： medical imaging least significant bit (LSB) watermarking LED lightweight cryptography parallel computing high-level synthesis (HLS) field-programmable gate array (FPGA)

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Development of methods for parallel processing of series of images obtained by a machine vision system in various electromagnetic ranges 2

Development of methods for parallel processing of series of ...

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Conference on Optical Instrument Science, Technology, and applications ii Held as Part of SPIE Optical System Design Conference

作者： Semenishchev, Evgenii Voronin, Viacheslav Alepko, Andrey Zelensky, Aleksandr Agaian, Sos Moscow State Tech Univ STANKIN Moscow Russia CUNY Dept Comp Sci New York NY 10314 USA

ISBN: (数字)9781510645974

ISBN: (纸本)9781510645974;9781510645967

Machine vision systems used in modern industrial complexes, based on the analysis of multi and hyperspectral imaging. The transition to implementing the "Industry 4.0" program is not possible when using one type of data. The first control system used only the visible range image. They made it possible to analyze the trajectories of movement of objects, control product quality, carry out security functions (control of perimeter crossing), etc. The development of new industrial robotic cells and processing complexes using cognitive functions implying the receipt, analysis, and processing of heterogeneous data. The construction of a unified information field, which allows performing multidimensional operations with data, allows increasing the speed of decision-making and the implementation of automated robot-human systems at the level of an assistant working in a unified workspace. The use of machine vision systems analyzing information received in: visible (shape, the trajectory of movement, position of objects, etc.);near-infrared range (data is similar to visible, allows operation in dusty, foggy, low light conditions);far-infrared range - thermal (plotting temperature gradients, identifying areas of overheating);ultraviolet range (analysis of ionization sources, corona discharges, static charges, tags);x-ray and microwave ranges (analysis of the surface and internal structure of objects, allow the identification of defects);range and 3D sensors (construction of volumetric figures, analysis of the relative position of objects and their interaction), etc. Data analysis is often performed not by a single camera but by a group of sensors located not in a single housing. Primary data integration reduces the number of information channels while maintaining the functionality and accuracy of the analysis. The article discusses creating fusion images obtained by industrial sensors into a combined image containing joint data. Combining multi and hyperspectral imaging makes i

关键词： preprocessing IR image local features denoising combining image fusion image

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Design of FPGA based Custom IP Core to Detect the Edges of Brain Tumors

Design of FPGA based Custom IP Core to Detect the Edges of B...

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International Symposium on VLSI Design and Test (VDAT)

作者： Soumita Chatterjee Soumya Pandit Arpita Das Institute of Radio Physics and Electronics University of Calcutta Kolkata India

ISBN: (数字)9798350380101

ISBN: (纸本)9798350380118

In the realm of medical imaging, identifying and classifying tumors using light weight machine learning algorithms require edge detection procedure. This renders the process of edge detection as a task of utmost importance which makes a difference between a right and wrong diagnosis. Due to its emergent nature , prompt and accurate diagnosis necessitates parallel and real-time processing. The process is made easier by the reconfigurable and parallel processing capabilities of field programmable gate array (FPGA). This paper presents the design and implementation of a custom Intellectual Property (IP) core for processing edge detection algorithm on FPGA. The proposed IP core is made to effectively use FPGA hardware acceleration to compute the edges of brain tumors. The scalable design of the core makes it simple to incorporate it into a range of FPGA-based systems and applications. The design of such custom IP core is achieved with the help of high level synthesis (HLS) which is then integrated and implemented on PYNQ Z2 board. The core is synthesized and tested to determine the performance metrics such as throughput, resource utilization, and power consumption. Effectiveness of the proposed IP core is demonstrated by experimental results. This IP core is designed in such a way that it processes the image and offers an adaptable and effective edge detection solution. This in turn opens the doors for integration into a range of FPGA-based real time applications and systems in various domain such as industrial automation, robotic surgery, and autonomous vehicles.

关键词： Power demand image edge detection Morphology Feature extraction Real-time systems IP networks Resource management Field programmable gate arrays Tumors Cancer

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Computational imaging modalities for multi-focal whole-slide imaging systems

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APPLIED OPTICS 2020年第20期59卷 5967-5982页

作者： van der Graaff, Leon van Leenders, Geert J. L. H. Boyaval, Fanny Stallinga, Sjoerd Delft Univ Technol Dept Imaging Phys Delft Netherlands Erasmus MC Dept Pathol Rotterdam Netherlands Leiden Univ Dept Pathol Med Ctr Leiden Netherlands

Whole-slide imaging systems can generate full-color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi-line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single-scan multi-focal whole-slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi-focal image data are studied. First, 3D imaging of thick absorption stained slides (similar to 60 mu m) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick-tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features com-plementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at similar to GPx/s throughputs. (C) 2020 Optical Society of America

关键词： image formation theory image processing image processing algorithms image quality imaging systems Three dimensional imaging

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GVLD: A Fast and Accurate GPU-Based Variational Light-Field Disparity Estimation Approach

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IEEE TRANSACTIONS ON CIRCUITS AND systems FOR VIDEO TECHNOLOGY 2021年第7期31卷 2562-2574页

作者： Tran, Trung-Hieu Mammadov, Gasim Simon, Sven Univ Stuttgart Inst Parallel & Distributed Syst D-70569 Stuttgart Germany

Disparity estimation is an essential task taking part in many light-field applications. Due to the complexity of algorithms and high dimensional property of light-field data, performing this task involves a significant computational effort and results in very long processing time on CPU. Graphics processing units (GPUs), which is capable of massively parallel processing, is a promising solution to cover the computation requirement and speed up the task. In this paper, we develop a GPU-accelerated approach for light-field disparity estimation using a variational computation framework (GVLD). Our algorithm combines the intrinsic sub-pixel precision of variational formulation and the effectiveness of weighted median filtering to produce a highly accurate solution. The proposed algorithm is fully parallelized and optimized for the implementation using the OpenCL framework. An intensive evaluation including a quantitative comparison to related works and a detailed analysis of the proposed approach's performance is presented. Experimental results demonstrate our superior performance compared to state-of-the-art approaches. The proposed approach is 10+ times faster than other approaches running on a similar GPU platform and provides the most accurate solution among optimization-based approaches. Compared to the implementation running on CPU, our GPU-accelerated method achieves up to 365x speed up.

关键词： Disparity estimation light-field image processing GPU acceleration OpenCL

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