In fluorescence microscopy, one can distinguish two kinds of imaging approaches, wide field and raster scan
microscopy, differing by their excitation and detection scheme. In both imaging modalities the acquisition is
independent of the information content of the image. Rather, the number of acquisitions N, is imposed by
the Nyquist-Shannon theorem. However, in practice, many biological images are compressible (or, equivalently
here, sparse), meaning that they depend on a number of degrees of freedom K that is smaller that their size N.
Recently, the mathematical theory of compressed sensing (CS) has shown how the sensing modality could take
advantage of the image sparsity to reconstruct images with no loss of information while largely reducing the number M of acquisition. Here we present a novel fluorescence microscope designed along the principles of CS. It uses a spatial light modulator (DMD) to create structured wide field excitation patterns and a sensitive point detector to measure the emitted fluorescence. On sparse fluorescent samples, we could achieve compression ratio N/M of up to 64, meaning that an image can be reconstructed with a number of measurements of only 1.5 % of its pixel number. Furthemore, we extend our CS acquisition scheme to an hyperspectral imaging system.
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