Results 241 to 250 of about 8,541 (271)

Distributed compressed sensing for image signals

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2014
Distributed compressed sensing (DCS) is able to exploit both intra-and inter-signal correlation structures of multi-signal ensemble. This paper proposes a DCS scheme for image signal compression and reconstruction. The key idea is to exploit the inter-correlation of the blocks that split from the image. Significantly, joint sparse model was employed to
Zongxin Yu, Rui Wang 0034, Haiyan Zhang, Yanliang Jin, Yixing Fu   +4 more
openaire   +1 more source

Distributed Compressed Sensing

2015
This chapter first introduces CS in the conventional setting where one device acquires one signal and sends it to a receiver, and then extends it to the distributed framework in which multiple devices acquire multiple signals. In particular, we focus on two key problems related to the distributed setting. The former is the definition of sparsity models
Giulio Coluccia, Chiara Ravazzi, Enrico Magli   +2 more
openaire   +1 more source

Measurement compression in distributed compressive video sensing

2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), 2010
In some application scenarios a video codec with simple encoder and complex decoder is desired. Distributed video coding (DVC) and compressive sensing (CS) theory proposed recently are two techniques suitable to such scenarios, and several video coding schemes that combine CS with DVC have appeared.
null Xiaoran Hao, null Bojin Zhuang, null Anni Cai   +2 more
openaire   +1 more source

Sensing matrix optimization in Distributed Compressed Sensing

2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009
Distributed Compressed Sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different.
Pablo Vinuelas-Peris, Antonio Artes-Rodriguez   +1 more
openaire   +1 more source

Number of compressed measurements needed for noisy distributed compressed sensing

2012 IEEE International Symposium on Information Theory Proceedings, 2012
In this paper, we consider a data collection network (DCN) system where sensors take samples and transmit them to a Fusion Center (FC). Signal correlation is modeled with signal sparseness. The number of compressed measurements which allows correct signal recovery at FC is investigated.
Sangjun Park 0002, Heung-No Lee
openaire   +1 more source

Noncoherent compressive sensing with application to distributed radar

2011 45th Annual Conference on Information Sciences and Systems, 2011
We consider a multi-static radar scenario with spatially dislocated receivers that can individually extract delay information only. Furthermore, we assume that the receivers are not phase-synchronized, so the measurements across receivers can only be combined noncoherently.
Christian R. Berger, José M. F. Moura
openaire   +1 more source

Distributed compressed sensing for block-sparse signals

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, 2011
To address the problems of high sampling rates, shadow fading and additive noise from the receiver, in this paper, a distributed compressed sampling (DCS) and centralized reconstruction approach which utilize the spatial diversity against fading channels is proposed.
Xing Wang, Wenbin Guo, Yang Lu, Wenbo Wang 0007   +3 more
openaire   +1 more source

Distributed compressed sensing for despeckling of SAR images

Digital Signal Processing, 2018
Abstract Speckle noise is one of the critical disturbances that present in the radar imagery. This noise degrades the quality of synthetic aperture radar (SAR) images and needs to be reduced before using SAR images. This paper investigates a novel method for despeckling of SAR images in the distributed compressed sensing (DCS) framework.
Ahmad Shafiei, Mojtaba Beheshti, Ehsan Yazdian   +2 more
openaire   +1 more source

Distributed compressive sensing in heterogeneous sensor network

Signal Processing, 2016
In this paper, we apply distributed compressive sensing (DCS) in heterogeneous sensor network (HSN). Combining different types of measurement matrices and different numbers of measurements, we firstly investigate three different scenarios in which HSN is used for signal acquisition.
Jing Liang 0002, Chengchen Mao
openaire   +1 more source

Perceptual-Based Distributed Compressed Video Sensing

2015 Data Compression Conference, 2015
This paper proposes an approach of compressed sensing (CS) of video in which distributed video coding DVC and CS are integrated as in [1], and the sensing matrix is modulated in suit of [2] but with proposed fixed weighting strategy to certain DCT coefficients in an effort to improve the visual quality of reconstruction.
Sawsan Abdellatif Abdelsalam Elsayed, Maha Mohamed Elsabrouty   +1 more
openaire   +1 more source