A High Performance Image Processing Unit for On-orbit Servicing

2006 ◽  
Author(s):  
Hiroshi Yamamoto ◽  
Yasufumi Nagai ◽  
Shinichi Kimura ◽  
Hiroshi Takahashi ◽  
Satoko Mizumoto ◽  
...  
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Processing Unit

Real-Time Implementation of Chirp Scaling Algorithm

2011 ◽  
Vol 58-60 ◽  
pp. 1113-1118
Author(s):  
Chang Zhen Shi ◽  
Zhen Song Wang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
High Performance ◽  
Phase Factor ◽  
Parallel Structure ◽  
Processing Unit ◽  
High Data ◽  
Huge Data ◽  
Chirp Scaling Algorithm ◽  
Parallel Fft

Echo data generated by Synthetic aperture radar (SAR) has the characteristics of high data rate and huge data quantity. However, the complex Chirp Scaling (CS) algorithm in SAR processing leads to excessive calculations. To solve this problem, this paper presents an implementation on a parallel structure real-time image processing board, which adopts principal-subordinate parallel processing structures. The principal FPGA board is responsible for the control of the entire image processing, data collection, distribution and interaction. Each subordinate FPGA, as an independent processing unit, is able to independently finish the FFT transformation and phase factor compensation. The performance of the high-performance parallel FFT processors is 4 times as efficient as that of a single butterfly processor.The phase factor generation and compensation is optimized through two steps, one is fast algorithm in phase factor generation and compensation; the other is the optimization among the processing procedures,The proposed architecture can process an image in size16384 × 65536 at 100MHZ operation frequency within 12.5s.

scholarly journals CMOS Image Sensor with On-Chip Image Compression: A Review and Performance Analysis

2010 ◽  
Vol 2010 ◽  
pp. 1-17 ◽  
Author(s):  
Milin Zhang ◽  
Amine Bermak
Keyword(s):  
Image Processing ◽  
Performance Analysis ◽  
Image Compression ◽  
High Performance ◽  
Cmos Technology ◽  
Image Sensor ◽  
Processing Unit ◽  
Cmos Image Sensors ◽  
Design Paradigm ◽  
On Chip

Demand for high-resolution, low-power sensing devices with integrated image processing capabilities, especially compression capability, is increasing. CMOS technology enables the integration of image sensing and image processing, making it possible to improve the overall system performance. This paper reviews the current state of the art in CMOS image sensors featuring on-chip image compression. Firstly, typical sensing systems consisting of separate image-capturing unit and image-compression processing unit are reviewed, followed by systems that integrate focal-plane compression. The paper also provides a thorough review of a new design paradigm, in which image compression is performed during the image-capture phase prior to storage, referred to as compressive acquisition. High-performance sensor systems reported in recent years are also introduced. Performance analysis and comparison of the reported designs using different design paradigm are presented at the end.

Matlab and Parallel Computing

2012 ◽  
Vol 17 (4) ◽  
pp. 207-216 ◽  
Author(s):  
Magdalena Szymczyk ◽  
Piotr Szymczyk
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Parallel Computing ◽  
Distributed Computing ◽  
Control Systems ◽  
High Performance ◽  
Parallel Applications ◽  
Process Simulations ◽  
Key Features ◽  
Financial Process

Abstract The MATLAB is a technical computing language used in a variety of fields, such as control systems, image and signal processing, visualization, financial process simulations in an easy-to-use environment. MATLAB offers "toolboxes" which are specialized libraries for variety scientific domains, and a simplified interface to high-performance libraries (LAPACK, BLAS, FFTW too). Now MATLAB is enriched by the possibility of parallel computing with the Parallel Computing ToolboxTM and MATLAB Distributed Computing ServerTM. In this article we present some of the key features of MATLAB parallel applications focused on using GPU processors for image processing.

scholarly journals Flexible and High Performance ASIPs for Pixel Level Image Processing and Two Dimensional Image Processing

2013 ◽  
Vol 21 (3) ◽  
pp. 552-562
Author(s):  
Hsuan-Chun Liao ◽  
Mochamad Asri ◽  
Tsuyoshi Isshiki ◽  
Dongju Li ◽  
Hiroaki Kunieda
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

Micro-Flow-Cytometer Integrated witch Optical Tweezer and DIP Technique for Cell/Particle Sorting and Manipulation

2007 ◽  
Vol 121-123 ◽  
pp. 1351-1354
Author(s):  
Yu Sheng Chien ◽  
Che Hsin Lin ◽  
Fu Jen Kao ◽  
Cheng Wen Ko
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Performance ◽  
Optical Tweezer ◽  
Processing Technique ◽  
Microfluidic System ◽  
Image Processing Technique ◽  
Target Cells ◽  
Digital Image Processing Technique

This paper proposes a novel microfluidic system for cell/microparticle recognition and manipulation utilizing digital image processing technique (DIP) and optical tweezer under microfluidic configuration. Digital image processing technique is used to count and recognize the cell/particle samples and then sends a control signal to generate a laser pulse to manipulate the target cell/particle optically. The optical tweezer system is capable of catching, moving and switching the target cells at the downstream of the microchannel. The trapping force of the optical tweezer is also demonstrated utilizing Stocks-drag method and electroosmotic flow. The proposed system provides a simple but high-performance solution for microparticle manipulation in a microfluidic device.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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