scholarly journals High-performance parallel image reconstruction for the New Vacuum Solar Telescope

2015 ◽  
Vol 67 (3) ◽  
pp. 47 ◽  
Author(s):  
Xue-Bao Li ◽  
Zhong Liu ◽  
Feng Wang ◽  
Zhen-Yu Jin ◽  
Yong-Yuan Xiang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
High Performance ◽  
Solar Telescope ◽  
Parallel Image

scholarly journals PARALLEL IMAGE RECONSTRUCTION FOR NEW VACUUM SOLAR TELESCOPE

2014 ◽  
Vol 47 (2) ◽  
pp. 43-47
Author(s):  
Xue-Bao Li ◽  
Feng Wang ◽  
Yong Yuan Xiang ◽  
Yan Fang Zheng ◽  
Ying Bo Liu ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Solar Telescope ◽  
Parallel Image

Enhanced Three-Dimensional Color Conversion Based on Discrete Wavelet Transform

2012 ◽  
Vol 155-156 ◽  
pp. 440-444
Author(s):  
He Yan ◽  
Xiu Feng Wang
Keyword(s):  
Information Technology ◽  
Wavelet Transform ◽  
Image Reconstruction ◽  
Image Quality ◽  
Image Compression ◽  
High Performance ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Compression Algorithms ◽  
Color Conversion

JPEG2000 algorithm has been developed based on the DWT techniques, which have shown how the results achieved in different areas in information technology can be applied to enhance the performance. Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. Wavelets have become a popular technology for information redistribution for high-performance image compression algorithms. Lossy compression algorithms sacrifice perfect image reconstruction in favor of improved compression rates while minimizing image quality lossy.

Improved parallel image reconstruction using feature refinement

2017 ◽  
Vol 80 (1) ◽  
pp. 211-223 ◽  
Author(s):  
Jing Cheng ◽  
Sen Jia ◽  
Leslie Ying ◽  
Yuanyuan Liu ◽  
Shanshan Wang ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Parallel Image

scholarly journals Efficiently Building On-line Tools for Distributed Heterogeneous Environments

2002 ◽  
Vol 10 (1) ◽  
pp. 67-74
Author(s):  
Günther Rackl ◽  
Thomas Ludwig ◽  
Markus Lindermeier ◽  
Alexandros Stamatakis
Keyword(s):  
Image Reconstruction ◽  
Software Development ◽  
High Performance ◽  
Development Process ◽  
Test Case ◽  
Heterogeneous Environments ◽  
Major Drawback ◽  
Performance Improvements ◽  
Performance Requirements ◽  
On Line

Software development is getting more and more complex, especially within distributed middleware-based environments. A major drawback during the overall software development process is the lack of on-line tools, i.e. tools applied as soon as there is a running prototype of an application. The MIMO MIddleware MOnitor provides a solution to this problem by implementing a framework for an efficient development of on-line tools. This paper presents a methodology for developing on-line tools with MIMO. As an example scenario, we choose a distributed medical image reconstruction application, which represents a test case with high performance requirements. Our distributed, CORBA-based application is instrumented for being observed with MIMO and related tools. Additionally, load balancing mechanisms are integrated for further performance improvements. As a result, we obtain an integrated tool environment for observing and steering the image reconstruction application. By using our rapid tool development process, the integration of on-line tools shows to be very convenient and enables an efficient tool deployment.

scholarly journals PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

2020 ◽  
Vol 10 (10) ◽  
pp. 3382
Author(s):  
Rahmat Ullah ◽  
Tughrul Arslan
Keyword(s):  
Image Reconstruction ◽  
High Performance Computing ◽  
High Performance ◽  
Reconstruction Algorithm ◽  
Apache Spark ◽  
Cloud Platform ◽  
Data Set ◽  
Image Reconstruction Algorithm ◽  
Performance Computing ◽  
Microwave Image

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

Parallel image reconstruction on MIMD computers for three-dimensional cone-beam tomography

1998 ◽  
Vol 24 (9-10) ◽  
pp. 1461-1479 ◽  
Author(s):  
C. Laurent ◽  
F. Peyrin ◽  
J.-M. Chassery ◽  
M. Amiel
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Cone Beam ◽  
Parallel Image ◽  
Dimensional Cone ◽  
Cone Beam Tomography
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