Multiple-prespecified-dictionary sparse representation for compressive sensing image reconstruction with nonconvex regularization

DOI10.1016/j.jfranklin.2018.12.013zbMath1455.94031OpenAlexW2909095597WikidataQ128575411 ScholiaQ128575411MaRDI QIDQ1730087

Li Xu, Guan Gui, Yunyi Li, Fei Dai, Xie-Feng Cheng

Publication date: 11 March 2019

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2018.12.013

Mathematics Subject Classification ID

Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items

Nonconvex \(L_{1/2}\)-regularized nonlocal self-similarity denoiser for compressive sensing based CT reconstruction ⋮ Smoothed amplitude flow-based phase retrieval algorithm ⋮ Noise learning based discriminative dictionary learning algorithm for image classification ⋮ Nonconvex nonsmooth low-rank minimization for generalized image compressed sensing via group sparse representation ⋮ A unified framework for nonconvex nonsmooth sparse and low-rank decomposition by majorization-minimization algorithm

Uses Software

• BLOOMP

Cites Work

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