Recovery of image and video based on compressive sensing via tensor approximation and Spatio-temporal correlation-Reference-Cited by-同舟云学术

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Recovery of image and video based on compressive sensing via tensor approximation and Spatio-temporal correlation

Published:2020-10-28 Issue:5 Volume:80 Page:7433-7450
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Zhong Yuanhong^ORCID,Zhang Jing,Zhou Zhaokun,Cheng Xinyu,Huang Guan,Li Qiang

Funder

National Natural Science Foundation

Technology Innovation and Application Development Project of Chongqing

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-020-09907-1.pdf

Reference36 articles.

1. Asif MS, Romberg J (2013) Fast and accurate algorithms for re-weighted ℓ1-norm minimization. IEEE Trans Signal Process 61(23):5905–5916

2. Baraniuk RG, Cevher V, Duarte MF, et al (2010) Model-based compressive sensing. IEEE Trans Inf Theory 56(4)1982–2001. https://doi.org/10.1109/TIT.2010.2040894

3. Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81

4. Candès EJ, Wakin MB, Boyd SP (2007) Enhancing sparsity by reweighted L1 minimization. J Fourier Anal Appl 14(5):877–905. https://doi.org/10.1007/s00041-008-9045-x

5. Candès EJ, Wakin MB, Boyd SP (2007) Enhancing sparsity by reweighted L1 minimization. J Fourier Anal Appl 14(5):877–905

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