Reducing Data Acquisition for Fast Structured Illumination Microscopy using Compressed Sensing

Abstract : In this work, we introduce an original strategy to apply the Compressed Sensing (CS) framework to a super-resolution Structured Illumination Microscopy (SIM) technique. We first define a framework for direct domain CS, that exploits the sparsity of fluorescence microscopy images in the Fourier domain. We then propose an application of this method to a fast 4-images SIM technique, which allows to reconstruct super-resolved fluorescence microscopy images using only 25% of the camera pixels for each acquisition.
Type de document :
Communication dans un congrès
14th International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017, Melbourne, Australia. 〈10.1109/isbi.2017.7950461 〉
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https://hal.archives-ouvertes.fr/hal-01623739
Contributeur : François Orieux <>
Soumis le : mercredi 25 octobre 2017 - 15:47:58
Dernière modification le : jeudi 22 novembre 2018 - 14:39:52

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William Meiniel, Piernicola Spinicelli, Elsa Angelini, Alexandra Fragola, Vincent Loriette, et al.. Reducing Data Acquisition for Fast Structured Illumination Microscopy using Compressed Sensing. 14th International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017, Melbourne, Australia. 〈10.1109/isbi.2017.7950461 〉. 〈hal-01623739〉

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