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© Research
Publication : IEEE International Symposium on Biomedical Imaging, ISBI

Compressed Sensing for Digital Holographic Microscopy

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in IEEE International Symposium on Biomedical Imaging, ISBI - 01 Jan 2010

Marcio Marim, Michael Atlan, Elsa Angelini and Jean-Christophe Olivo-Marin

IEEE International Symposium on Biomedical Imaging, ISBI, 2010. Best Student Paper Award

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 quadratureresolved 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.