Practical compressive sensing of large images

Authors:
Yair Rivenson;Adrian Stern
Affiliations:
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel;Electro-Optics Department, Ben-Gurion University of the Negev, Beer-Sheva, Israel
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

Citing 5
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Abstract

Compressive imaging (CI) is a natural branch of compressed sensing (CS). One of the main difficulties in implementing CI is that, unlike many other CS applications, it involves huge amount of data. This data load has extensive implications for the complexity of the optical design, for the complexity of calibration, for data storage requirements. As a result, practical CI implementations are mostly limited to relative small image sizes. Recently we have shown that it is possible to overcome these problems by using a separable imaging operator. We have demonstrated that separable imaging operator permits CI of megapixel size images and we derived a theoretical bound for oversampling factor requirements. Here we further elaborate the tradeoff of using separable imaging operator, present and discuss additional experimental results.