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18th image processing: algorithms and systems Conference, IPAS 2020

作者： Trongtirakul, Thaweesak Chiracharit, Werapon Agaian, Sos Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Electronics and Telecommunication Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Dept. Computer Sciences CUNY Graduate Center College of Staten Island New York United States

Multispectral images captured by near-infrared (NIR) filtered devices have an attractive potential for numerous applications in computer vision, robot vision, and an artificial intelligence system. Although near-infrared (NIR) filtered devices display some unseen details, but their colors contain false colors affected main color components. Therefore, the color restoration of near-infrared (NIR) filtered images is a challenge for human perception. To overcome the challenge, the proposed method has four main steps: i) color balance with chromatic adaptation;ii) color component switching;iii) Hue-Saturation-Lightness adjustment;and iv) image fusion using a proportional-weighing metric. The proposed results achieve to: i) restore RGB color component, ii) remove the color-overlapped effect and generate structure details close to the ground truth images;and iii) preserve overall brightness. © 2020, Society for imaging Science and Technology

关键词： Color

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Core imaging Library-Part ii: multichannel reconstruction for dynamic and spectral tomography

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PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2021年第2204期379卷 20200193-20200193页

作者： Papoutsellis, Evangelos Ametova, Evelina Delplancke, Claire Fardell, Gemma Jorgensen, Jakob S. Pasca, Edoardo Turner, Martin Warr, Ryan Lionheart, William R. B. Withers, Philip J. Univ Manchester Henry Royce Inst Dept Mat Manchester Lancs England Univ Manchester Dept Math Manchester Lancs England Univ Manchester Res IT Serv Manchester Lancs England Sci Technol Facil Council UK Res & Innovat Rutherford Appleton Lab Sci Comp Dept Didcot Oxon England Univ Bath Dept Math Sci Bath Avon England Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Karlsruhe Inst Technol Lab Applicat Synchrotron Radiat Karlsruhe Germany

The newly developed core imaging library (CIL) is a flexible plug and play library for tomographic imaging with a specific focus on iterative reconstruction. CIL provides building blocks for tailored regularized reconstruction algorithms and explicitly supports multichannel tomographic data. In the first part of this two-part publication, we introduced the fundamentals of CIL. This paper focuses on applications of CIL for multichannel data, e.g. dynamic and spectral. We formalize different optimization problems for colour processing, dynamic and hyperspectral tomography and demonstrate CIL's capabilities for designing state-of-the-art reconstruction methods through case studies and code snapshots. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.

关键词： x-ray CT sparse CT positron emission tomography (PET) magnetic resonance imaging (MRI) materials science computed tomography inverse problems iterative reconstruction

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Hybrid Detector with Interpixel Communication for Color x-ray imaging

Hybrid Detector with Interpixel Communication for Color X-ra...

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IEEE International Conference on Electronics, Circuits and systems (ICECS)

作者： P. Grybos R. Kleczek P. Kmon A. Krzyzanowska P. Otfinowski R. Szczygiel M. Zoladz AGH University of Science and Technology Krakow Poland

This paper presents a readout integrated circuit of pixel architecture called MPIx (Multithreshold PIxels), designed for CdTe pixel detectors used in high-energy x-ray imaging applications. The MPIx IC of the area of $9.6\ \text{mm} \times 20.3\ \text{mm}$ is designed in a CMOS 130 nm process. The IC core is a matrix of $96\times 192$ square-shaped pixels of $100\ \mu \mathrm{m}$ pitch. Each pixel contains an analog front-end, four independently working discriminators, and four 12-bit ripple counters. Such pixel architecture allows photon processing one by one and selecting the x-ray photons according to their energy (x-ray color imaging). The MPIx chips are bump-bonded to pixel CdTe sensor with a pitch of $100\ \mu \mathrm{m}$ and thickness of $750\ \mu \mathrm{m}$ . These hybrid detectors (sensor bump-bonded to readout integrated circuit) are characterized by test pulses and x-ray radiation. To match the different range of applications the MPIx chip has 8 possible different gain settings. In the high gain mode, the chip can operate with xray photons up to 46 keV and has the Equivalent Noise Charge (ENC) of 123 el. rms. In the low gain mode, the ENC is equal to 192 el. rms, and the chip can process the x-ray photons of energy up to 154 keV. The matrix of 18432 pixels has a very good uniformity: globally set threshold has an effective pixel to pixel offset spread of 1 mV (for the full threshold range up to 800 mV). The power consumption per pixel is $80\ \mu \mathrm{W}/\text{pixel}$ . Additionally, to deal with charge sharing effects in a thick semiconductor pixel sensor, Multithreshold Pattern Recognition algorithms are implemented in the MPIx IC.

关键词： Integrated circuits Power demand ii-VI semiconductor materials image color analysis Radiation detectors Detectors Pattern recognition

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Radix-4³ based two-dimensional FFT architecture with efficient data reordering scheme

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IET COMPUTERS AND DIGITAL TECHNIQUES 2019年第2期13卷 78-86页

作者： Kala, S. Mathew, Jimson Jose, Babita R. Nalesh, S. Cochin Univ Sci & Technol Sch Engn Kochi 22 Kerala India Indian Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Cochin Univ Sci & Technol Dept Elect Kochi 22 Kerala India

Multi-dimensional Discrete Fourier Transforms (DFTs) play an important role in signal and image processing applications. image reconstruction is a key component in signal processing applications like medical imaging, computer vision, face recognition etc. Two dimensional fast Fourier Transform (2D FFT) and Inverse FFT plays vital role in reconstruction. In this paper we present a fast 64 x 64 point 2D FFT architecture based on radix-43 algorithm using a parallel unrolled radix-4(3) FFT as the basic block. Our radix-4(3) architecture is a memory optimized parallel architecture which computes 64-point FFT, with least execution time. Proposed architecture produces reordered output of both 64-point one dimensional (1D) FFT and 64 x 64 point 2D FFT, without using any additional hardware for reordering. The proposed architecture has been implemented in UMC 40nm CMOS technology with clock frequency of 500 MHz and area of 0.841 mm(2). The power consumption of proposed architecture is 358 mW at 500 MHz. Energy efficiency (FFTs computed per unit of energy) is 341 points/Joule. Computation time of 64 x 64 point FFT is 8.19 mu s. ASIC implementation results shows better performance of proposed work in terms of computation time when compared with state-of-art implementation. Proposed architecture has also been implemented in Virtex-7 FPGA which gives comparable area.

关键词： application specific integrated circuits parallel architectures field programmable gate arrays computational complexity discrete Fourier transforms fast Fourier transforms CMOS logic circuits two-dimensional FFT architecture signal processing systems image reconstruction signal processing applications medical imaging computer vision FFT computation parallel unrolled radix-4 memory optimised parallel architecture data reordering discrete Fourier transforms multidimensional DFT two-dimensional fast Fourier transform application specific integrated circuit face recognition 2D FFT architecture radix-4(3) algorithm row-column decomposition UMC complementary metal oxide semiconductor power consumption Virtex 7 field programmable gate array power 358 0 mW frequency 500 0 MHz time 8 19 mus size 40 0 nm size 0 841 mm

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Research on parallel algorithms for uv-faceting imaging

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Chinese Astronomy and Astrophysics 2019年第3期43卷 424-443页

作者： Bao-qiang, Lao Tao, An Ang, Yu Shao-guang, Guo Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai 200030 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Nanjing 210008 China School of Information and Communication Guilin University of Electronic Technology Guilin 541004 China

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility. © 2019

关键词： general image processing—radio continuum Instrumentation Interferometers—methods interferometric—techniques observational—techniques

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Polarimetric imaging microscopy in real-time 2

Polarimetric imaging microscopy in real-time

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Unconventional Optical imaging ii 2020

作者： Llaguno, Juan M. Fernández, Ariel Instituto de Física Facultad de Ingenieriá Universidad de la República J. Herrera y Reissig 565 Montevideo Uruguay

ISBN: (数字)9781510634756

ISBN: (纸本)9781510634749

imaging polarimetry allows visual information to be extracted from the polarization of light with recent applications ranging from remote sensing from satellite images to the study of anisotropic properties of biomolecules, as well as the development of denoising algorithms. In particular polarized light microscopy can reveal {among other properties- particular changes in the structure of some cells. In conventional approaches to polarimetric microscopy a sample is imaged by rotating polarizer and analizer so variable polarization states are achieved under time-division strategies. We propose a space-division multiplexing technique which by multiview sensing of a microscopic sample with an adequate polarization mask allows us to obtain Stokes parameters of the sample in real-time. Validation experiments obtaining Degree of Linear Polarization as well as Angle of Polarization of the sample are presented. © COPYRIGHT SPIE.

关键词： image processing

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Improved pedestrian detection by adjustment of segmented ROI in thermal night vision

Improved pedestrian detection by adjustment of segmented ROI...

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Signal processing algorithms, Architectures, Arrangements and applications (SPA)

作者： Karol Piniarski Paweł Pawłowski Adam Dąbrowski Division of Signal Processing and Electronic Systems Jana Pawła II 24 60-965 Poznan University of Technology Faculty of Automation Robotics and Electrical Engineering Institute of Automation and Robotics Poznań Poland

ISBN: (数字)9788362065394

ISBN: (纸本)9781728177465

In this work we present an analysis of the region of interest (ROI) generation from the source thermal night vision images through the double thresholding segmentation technique as a part of pedestrian detection procedure. In some cases, pedestrians do not fit into the generated ROIs. To solve the problem we propose to adjust (slightly enlarge) the segmented ROI. Through this, it is possible to reduce miss rate for the aggregated channel feature (ACF) classifier from 29.1% to 24.8% and for the deep convolutional neural network (CNN) classifier from 24.0% to 22.4%, with negligible impact on the processing time.

关键词： image segmentation Finite impulse response filters Thresholding (imaging) Cameras image resolution image edge detection Training

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A knowledge-based question answering system to provide cognitive assistance to radiologists

A knowledge-based question answering system to provide cogni...

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Conference on Medical imaging - imaging Informatics for Healthcare, Research, and applications

作者： Pillai, Anup Katouzian, Amin Kanjaria, Karina Shivade, Chaitanya Jadhav, Ashutosh Bendersky, Marina Mukherjee, Vandana Syeda-Mahmood, Tanveer IBM Res San Jose CA 95120 USA

ISBN: (纸本)9781510625556;9781510625563

With the advent of computers and natural language processing, it is not surprising to see that humans are trying to use computers to answer questions. By the 1960s, there were systems implemented on the two major models of question answering, IR-based and knowledge-based, to answer questions about sport statistics and scientific facts. This paper reports on the development of a knowledge-based question answering system that is aimed at providing cognitive assistance to radiologists. Our system represents the question as a semantic query to a medical knowledge base. Evidence obtained from textual and imaging data associated with the question is then combined to arrive at an answer. This question answering system has 3 stages: i) question text and answer choices processing, ii) image processing, and iii) reasoning. Currently, the system can answer differential diagnosis and patient management questions, however, we can tackle a wider variety of question types by improving our medical knowledge coverage in the future.

关键词： Question answering system biomedical domain image processing reasoning decision support system

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Novel Implementation Approach with Enhanced Memory Access Performance of MGS Algorithm for VLIW Architecture

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JOURNAL OF CIRCUITS systems AND COMPUTERS 2020年第12期29卷

作者： Najoui, Mohamed Hatim, Anas Belkouch, Said Chabini, Noureddine Mohamed V Univ E2SN Team ENSET Rabat Rabat Morocco Cadi Ayyad Univ TIM Team ENSA Marrakech Marrakech Morocco Univ Cadi Ayyad LGECOS Lab ENSA Marrakech Marrakech Morocco Royal Mil Coll Canada Dept Elect & Comp Engn Kingston ON Canada

Modified Gram-Schmidt (MGS) algorithm is one of the most-known forms of QR decomposition (QRD) algorithms. It has been used in many signal and image processing applications to solve least square problem and linear equations or to invert matrices. However, QRD is well-thought-out as a computationally expensive technique, and its sequential implementation fails to meet the requirements of many real-time applications. In this paper, we suggest a new parallel version of MGS algorithm that uses VLIW (Very Long Instruction Word) resources in an efficient way to get more performance. The presented parallel MGS is based on compact VLIW kernels that have been designed for each algorithm step taking into account architectural and algorithmic constraints. Based on instruction scheduling and software pipelining techniques, the proposed kernels exploit efficiently data, instruction and loop levels parallelism. Additionally, cache memory properties were used efficiently to enhance parallel memory access and to avoid cache misses. The robustness, accuracy and rapidity of the introduced parallel MGS implementation on VLIW enhance significantly the performance of systems under severe rea-time and low power constraints. Experimental results show great improvements over the optimized vendor QRD implementation and the state of art.

关键词： QR decomposition parallel MGS ILP DLP low-power DSP VLIW software pipelining signal and image processing

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Efficient graphical-processor-unit parallelization algorithm for computing Eigen values

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JOURNAL OF ELECTRONIC imaging 2020年第6期29卷 063008-063008页

作者： Ben Sayadia, Sofien Elloumi, Yaroub Akil, Mohamed Bedoui, Mohamed Hedi Univ Gustave Eiffel CNRS ESIEE Paris Gaspard Monge Comp Sci Lab Marne La Vallee France Univ Monastir Med Technol & Image Proc Lab Fac Med Monastir Tunisia Univ Sousse ISITCom Sousse Tunisia

Several leading-edge applications such as pathology detection, biometric identification, and face recognition are based mainly on blob and line detection. To address this problem, Eigen value computing has been commonly employed due to its accuracy and robustness. However, Eigen value computing requires a raised computational processing, intensive memory data access, and data overlapping, which involve higher execution times. To overcome these limitations, we propose in this paper a new parallel strategy to implement Eigen value computing using a graphics processing unit (GPU). Our contributions are (1) to optimize instruction scheduling to reduce the computation time, (2) to efficiently partition processing into blocks to increase the occupancy of streaming multiprocessors, (3) to provide efficient input data splitting on shared memory to benefit from its lower access time, and (4) to propose new data management of shared memory to avoid access memory conflict and reduce memory bank accesses. Experimental results show that our proposed GPU parallel strategy for Eigen value computing achieves speedups of 27 compared with a multithreaded implementation, of 16 compared with a predefined function in the OpenCV library, and of eight compared with a predefined function in the Cublas library, all of which are performed into a quad core multi-central-processing unit platform. Next, our parallel strategy is evaluated through an Eigen value-based method for retinal thick vessel segmentation, which is essential for detecting ocular pathologies. Eigen value computing is executed in 0.017 s when using Structured Analysis of the Retina database images. Accordingly, we achieved real-time thick retinal vessel segmentation with an average execution time of about 0.039 s. (C) 2020 SPIE and IS&T

关键词： Eigen values Hessian filter parallel algorithms graphics processing unit CUDA real-time graphics processing unit implementation

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