分布式并行计算在超分辨率图像增强中的应用

doi:10.11959/j.issn.1000-0801.2015103

Abstract

Abstract:

The distributed parallel computing in the super-resolution image enhancement applications was introduced.Super resolution image enhancement refers to improving the image resolution of 1080P video to 4K by software without upgrading existing acquisition device and increasing the transmission bandwidth.Due to the fact that super resolution image enhancement technology needs enormous computing power,super resolution image enhancement system and performance comparison testing by distributed parallel computing was studied.The conclusion shows that the super-image enhancement processing ability can be enhanced hundreds of times with distributed parallel computing technology,so that the production capacity of 4K video can be improved in rea1-world application.

Key words: super resolution, image enhancement, distributed computing, parallel computing

Jie Zheng,Sheng Bao,Ying Yang. Application of Distributed Parallel Computing in Super-Resolution Image Enbancement[J]. Telecommunications Science, 2015, 31(4): 37-42.

Figures/Tables 6

References 7

1	高传善．分布式系统设计．北京：机械工业出版社， 2001 Gao C S . The Design of Distributed Systems. Beijing: Machinery Industry Press, 2001
2	Dollimore J , Coulouris G . Distributed Systems:Concepts and Design, Fifth Edition. Beijing:Machinery Industry Press, 2012
3	李文军，万海．分布式计算．北京：机械工业出版社， 2012 Li W J Wan H . Distributed computing. Beijing: Machinery Industry Press, 2012
4	Pacheco P S ．并行程序设计导论．北京：机械工业出版社， 2013 Pacheco P S . An Introduction to Parallel Programming. Beijing: Machinery Industry Press, 2013
5	夏卫民．并行操作系统原理与技术．北京：国防工业出版社， 2002 Xia W M . The Principle and Technique of Parallel Operating System. Beijing: National Defense Industry Press, 2002
6	杨欣．图像超分辨率技术原理及应用．北京：国防工业出版社， 2013 Yang X . Image Super Resolution Technology Principle and Application. Beijing: National Defense Industry Press, 2013
7	Hughes C , Hughes T . Parallel and Distributed Programming Using C++. Beijing: Chinese Power Press, 2004

Metrics

Recommended 0

No Suggested Reading articles found!

不同情况	不使用分布式并行计算	2台主机参与计算	4台主机参与计算
效率	1:240	1:5.7	1:2.5

[1]	Ping XIE,Xiaosong LIU. Security of the deployment of SDN-based IoT using blockchain [J]. Telecommunications Science, 2020, 36(12): 139-146.
[2]	Kun LI,Jing ZHANG,Xiao LI,Shi JIN. An overview of artificial intelligence assisted channel estimation [J]. Telecommunications Science, 2020, 36(10): 46-55.
[3]	Jianxin Liao. Big Data Technology：Current Applications and Prospects [J]. Telecommunications Science, 2015, 31(7): 1-12.
[4]	Jianxin Ren,Huahui Chen. An Adaptive Subspace Similarity Search Approach [J]. Telecommunications Science, 2015, 31(7): 63-74.
[5]	Yong Liu,Jiangtao Luo,Shengxiong Deng,Xiaoping Wang. Diffluent Internet Traffic and Characteristics Computation Based on Hadoop [J]. Telecommunications Science, 2014, 30(12): 76-81.
[6]	Jie Liu,Zhe Wang. Construction of Carrier Business Precise Operating Platform Basedon Big Data [J]. Telecommunications Science, 2013, 29(3): 22-26.
[7]	Julong Lan,Dongnian Cheng,Yu Wang,Fengyu Zhang. Initial Analysis on Intelligence Mechanisms of Reconfigurable Network Abstract Flexibility,with which a reconfigurable network adaptively tunes its structure,plays a critical role in accommodating varieties of applications with desirable service levels.Most studies avalaible,however,rarely provide clear answers about the mechanism of flexibility that supports network reconfigurations.For the mechanism more clear,this paper clarifies a key target of the consistant matching of service outcomes to application demands.What is then revealed includes the kernal characteristic,so-called outcome following demand with gardually tuning,of the reconfiguration flexibility.Furthermore,four inherent schemes and frameworks representing reconfiguration flexibility are respectively examined in depth.They are the knowledeg-driven architecture of network functionalities,cross-layer interactions of functionalities,distributed and collaborative sensing,and distributed intelligece.Finally,the method of ant conoly optimization（ACO）is emphasized by explaining its effectiveness in optimizing non-linear problems such as performence-constrained routing at small time scales.Specifically,both the advantage and significance of emergence,the prominent feature of ACO,are illustrated. [J]. Telecommunications Science, 2012, 28(8): 105-112.
[8]	Jianfeng Wen,Yirong Shi. Research on Basing Distributed Computing Framework to Achieve Accelerating the Data Analysis Application of Telecommunications [J]. Telecommunications Science, 2012, 28(2): 22-26.
[9]	Wenwu Zheng,Xianxu Li,Zhiqin Huang. Technolical Analysis of Parallel Computing in Cloud Computing [J]. Telecommunications Science, 2011, 27(12): 31-38.
[10]	Huayun Yan,Jihong Guan. Survey of Bloom Filter [J]. Telecommunications Science, 2010, 26(2): 31-36.

Application of Distributed Parallel Computing in Super-Resolution Image Enbancement

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 7

Related Articles 10

Metrics

Recommended 0