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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper concerns solving the sparse deconvolution and demixing problemusing $\ell_{1,2}$-minimization. We show that under a certain structured randommodel, robust and stable recovery is possible. The results extend results ofLing and Strohmer [Self Calibration and Biconvex Compressive Sensing, InverseProblems, 2015], and in particular theoretically explain certain experimentalfindings from that paper. Our results do not only apply to the deconvolutionand demixing problem, but to recovery of column-sparse matrices in general.

Sparse Blind Deconvolution and Demixing Through $\ell_{1,2}$-Minimization