Compressed sensing in fluorescence microscopy. (January 2022)

• Record Type: Journal Article
• Authors: Calisesi, Gianmaria; Ghezzi, Alberto; Ancora, Daniele; D'Andrea, Cosimo; Valentini, Gianluca; Farina, Andrea; Bassi, Andrea
• Is Part Of: Progress in biophysics and molecular biology. Volume 168(2022)
• Page Start: 66
• Page End: 80
• Publication Date: 2022-01
• Subjects: Compressed sensing -- Optical imaging -- Fluorescence microscopy -- Inverse problems -- Biomedical imaging -- Computational imaging; Biophysics -- Periodicals; Biochemistry -- Periodicals; Biophysics -- Periodicals; Molecular Biology -- Periodicals; Biophysique -- Périodiques; Biochimie -- Périodiques; 571.4
• DOI: 10.1016/j.pbiomolbio.2021.06.004
• Languages: English
• ISSNs: 0079-6107

Abstract

Abstract: Compressed sensing (CS) is a signal processing approach that solves ill-posed inverse problems, from under-sampled data with respect to the Nyquist criterium. CS exploits sparsity constraints based on the knowledge of prior information, relative to the structure of the object in the spatial or other domains. It is commonly used in image and video compression as well as in scientific and medical applications, including computed tomography and magnetic resonance imaging. In the field of fluorescence microscopy, it has been demonstrated to be valuable for fast and high-resolution imaging, from single-molecule localization, super-resolution to light-sheet microscopy. Furthermore, CS has found remarkable applications in the field of mesoscopic imaging, facilitating the study of small animals' organs and entire organisms. This review article illustrates the working principles of CS, its implementations in optical imaging and discusses several relevant uses of CS in the field of fluorescence imaging from super-resolution microscopy to mesoscopy.