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AuthorElleuch, Ines
AuthorAbdelkefi, Fatma
AuthorSiala, Mohamed
AuthorHamila, Ridha
AuthorAl-Dhahir, Naofal
Available date2021-04-11T11:07:19Z
Publication Date2016
Publication NameEuropean Signal Processing Conference
ResourceScopus
URIhttp://dx.doi.org/10.1109/EUSIPCO.2016.7760293
URIhttp://hdl.handle.net/10576/18204
AbstractIn this paper, we address the problem of sparse signal recovery from scalar quantized compressed sensing measurements, via optimization. To compensate for compression losses due to dimensionality reduction and quantization, we consider a cost function that is more sparsity-inducing than the commonly used ?1-norm. Besides, we enforce a quantization consistency constraint that naturally handles the saturation issue. We investigate the potential of the recent Graduated-Non-Convexity based reweighted ?1-norm minimization for sparse recovery over polyhedral sets. We demonstrate by simulations, the robustness of the proposed approach towards saturation and its significant performance gain, in terms of reconstruction accuracy and support recovery capability.
Languageen
PublisherEuropean Signal Processing Conference, EUSIPCO
SubjectConcave approximation
Graduated-non-convexity
Quantized compressed sensing
Reweighted ?1
Support recovery
TitleQuasi-sparsest solutions for quantized compressed sensing by graduated-non-convexity based reweighted ?1minimization
TypeConference Paper
Pagination473-477
Volume Number2016-November


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