Compressed sensing for digital holographic microscopy

Authors:
Marcia M. Marim;Michael Atlan;Elsa D. Angelini;J.-C. Olivo-Marin
Affiliations:
Institut Pasteur, Unité d'Analyse d'Images Quantitative, CNRS, URA, Paris;Institut Langevin, ESPCI ParisTech, CNRS UMR, INSERM, UPMC, Paris;Institut Télécom, Télécom ParisTech, CNRS, LTCI, Paris;Institut Pasteur, Unité d'Analyse d'Images Quantitative, CNRS, URA, Paris
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

This paper describes an original microscopy imaging framework successfully employing Compressed Sensing for digital holography. Our approach combines a sparsity minimization algorithm to reconstruct the image and digital holography to perform quadrature-resolved random measurements of an optical field in a diffraction plane. Compressed Sensing is a recent theory establishing that near-exact recovery of an unknown sparse signal is possible from a small number of non-structured measurements. We demonstrate with practical experiments on holographic microscopy images of cerebral blood flow that our CS approach enables optimal reconstruction from a very limited number of measurements while being robust to high noise levels.