English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we study the compressed sensing reconstruction problem withgeneralized elastic net prior (GENP), where a sparse signal is sampled via anoisy underdetermined linear observation system, and an additional initialestimation of the signal (the GENP) is available during the reconstruction. Wefirst incorporate the GENP into the LASSO and the approximate message passing(AMP) frameworks, denoted by GENP-LASSO and GENP-AMP respectively. We theninvestigate the parameter selection, state evolution, and noise-sensitivityanalysis of GENP-AMP. We show that, thanks to the GENP, there is no phasetransition boundary in the proposed frameworks, i.e., the reconstruction erroris bounded in the entire plane. The error is also smaller than those of thestandard AMP and scalar denoising. A practical parameterless version of theGENP-AMP is also developed, which does not need to know the sparsity of theunknown signal and the variance of the GENP. Simulation results are presentedto verify the efficiency of the proposed schemes.

Approximate Message Passing-based Compressed Sensing Reconstruction with Generalized Elastic Net Prior