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research-article

Tensor compressed video sensing reconstruction by combination of fractional-order total variation and sparsifying transform

Authors:

Jiashu ZhangAuthors Info & Claims

Image Communication, Volume 55, Issue C

Pages 146 - 156

https://doi.org/10.1016/j.image.2017.03.021

Published: 01 July 2017 Publication History

Abstract

High reconstructed performance compressed video sensing (CVS) with low computational complexity and memory requirement is very challenging. In order to reconstruct the high quality video frames with low computational complexity, this paper proposes a tensor-based joint sparseness regularization CVS reconstruction model FrTVCST (fractional-order total variation combined with sparsifying transform), in which a high-order tensor fractional-order total variation (FrTV) regularization and a tensor discrete wavelet transform (DWT) L0 norm regularization are combined. Furthermore, an approach for choosing the regularization parameter that controls the influence of the two terms in this joint model is proposed. Afterwards, a tensor gradient projection algorithm extended from smoothed L0 (SL0) is deduced to solve this combined tensor FrTV and DWT joint regularization constrained minimization problem, using a smooth approximation of the L0 norm. Compared with several state-of-the-art CVS reconstruction algorithms, such as the Kronecker compressive sensing (KCS), generalized tensor compressive sensing (GTCS), N-way block orthogonal matching pursuit (N-BOMP), low-rank tensor compressive sensing (LRTCS), extensive experiments with commonly used video data sets show the competitive performance of the proposed algorithm with respect to the peak signal-to-noise ratio (PSNR) and subjective visual quality. A FrTV combined with tensor DWT for CS video reconstruction model is proposed.A method for estimating the regularization parameter is proposed.A tensor smoothed L0 algorithm is developed to solve this reconstruction model.The algorithm has a higher PSNR and better detail preservation.

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Cited By

Chen JChen ZSu KPeng ZLing N(2020)Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation modelJournal of Visual Communication and Image Representation10.1016/j.jvcir.2019.10273466:COnline publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.jvcir.2019.102734

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Published In

cover image Image Communication

Image Communication Volume 55, Issue C

July 2017

162 pages

ISSN:0923-5965

Issue’s Table of Contents

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 July 2017

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Cited By

Chen JChen ZSu KPeng ZLing N(2020)Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation modelJournal of Visual Communication and Image Representation10.1016/j.jvcir.2019.10273466:COnline publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.jvcir.2019.102734

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