Year: 2013
Type: preprint
Abstract: The mathematical theory of compressed sensing (CS) asserts that one can acquire signals from measurements whose rate is much lower than the total bandwidth. Whereas the CS theory is now well developed, challenges concerning hardware implementations of CS-based acquisition devices-especially in optics-have only started being addressed. This paper presents an implementation of compressive sensing in fluorescence microscopy and its applications to biomedical imaging. Our CS microscope combines a dynamic structured wide-field illumination and a fast and sensitive single-point fluorescence detection to enable reconstructions of images of fluorescent beads, cells, and tissues with undersampling ratios (between the number of pixels and number of measurements) up to 32. We further demonstrate a hyperspectral mode and record images with 128 spectral channels and undersampling ratios up to 64, illustrating the potential benefits of CS acquisition for higher-dimensional signals, which typically exhibits extreme redundancy. Altogether, our results emphasize the interest of CS schemes for acquisition at a significantly reduced rate and point to some remaining challenges for CS fluorescence microscopy.
Source: arXiv (Cornell University)
Institutions: Centre National de la Recherche Scientifique, Institut Interdisciplinaire de Neuroscience, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Paris-Saclay - Etablissement de Saclay, Institut de Recherche sur les Lois Fondamentales de l'Univers, Université Pierre-et-Marie-Curie, Stanford University, Laboratoire Kastler Brossel
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1666744270