NDLI: Sparse filter correlation model based joint reconstruction in distributed compressive video sensing

Content Provider	IEEE Xplore Digital Library
Author	Xun Wang Hao Fang Xuqi Zhu Bin Li Yu Liu
Copyright Year	2010
Description	Author affiliation: School of Information and Communication Engineering, Key Lab of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China (Xun Wang; Hao Fang; Xuqi Zhu; Bin Li; Yu Liu)
Abstract	Distributed compressive sensing (DCS) is a new technique that provides a low-complexity sub-Nyquist signal acquisition and reconstruction via a small number of random linear projections. In this paper, we propose sparse filter correlation model (SFCM) to exploit the correlations among successive video frames under the framework of distributed compressive video sensing (DCVS). At the central decoder, joint reconstruction is accomplished with the assistance of modified belief propagation (BP) algorithm, which is an efficient method for solving Bayesian inference problem. Simulation results illustrate that the proposed method provides better PSNR performance than joint sparse model 1 (JSM1) for DCVS.
Starting Page	483
Ending Page	487
File Size	106296
Page Count	5
File Format	PDF
ISBN	9781424468515
e-ISBN	9781424468539
DOI	10.1109/ICNIDC.2010.5657992
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Sparse filter correlation model Correlation PSNR Compressive sensing DCVS Filtering theory Decoding Sensors Joints Belief propagation
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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