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

The realisation of sensing modalities based on the principles of compressedsensing is often hindered by discrepancies between the mathematical model ofits sensing operator, which is necessary during signal recovery, and its actualphysical implementation, which can amply differ from the assumed model. In thispaper we tackle the bilinear inverse problem of recovering a sparse inputsignal and some unknown, unstructured multiplicative factors affecting thesensors that capture each compressive measurement. Our methodology relies oncollecting a few snapshots under new draws of the sensing operator, andapplying a greedy algorithm based on projected gradient descent and theprinciples of iterative hard thresholding. We explore empirically the samplecomplexity requirements of this algorithm by testing its phase transition, andshow in a practically relevant instance of this problem for compressive imagingthat the exact solution can be obtained with only a few snapshots.

A Greedy Blind Calibration Method for Compressed Sensing with Unknown Sensor Gains