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The mathematical theory of compressed sensing (CS) asserts that one canacquire signals from measurements whose rate is much lower than the totalbandwidth. Whereas the CS theory is now well developed, challenges concerninghardware implementations of CS-based acquisition devices-especially inoptics-have only started being addressed. This paper presents an implementationof compressive sensing in fluorescence microscopy and its applications tobiomedical imaging. Our CS microscope combines a dynamic structured wide-fieldillumination and a fast and sensitive single-point fluorescence detection toenable reconstructions of images of fluorescent beads, cells, and tissues withundersampling ratios (between the number of pixels and number of measurements)up to 32. We further demonstrate a hyperspectral mode and record images with128 spectral channels and undersampling ratios up to 64, illustrating thepotential benefits of CS acquisition for higher-dimensional signals, whichtypically exhibits extreme redundancy. Altogether, our results emphasize theinterest of CS schemes for acquisition at a significantly reduced rate andpoint to some remaining challenges for CS fluorescence microscopy.

Hyperspectral fluorescence microscopy based on Compressive Sampling