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Image Scanning Microscopy (ISM) enables super-resolution at an excellent signal-to-noise ratio thanks to a detector array. The microscope collects a confocal-like image for each detector element, generating a large dataset that requires tailored processing tools to be converted into a single super-resolved image. We propose a novel algorithm to fuse the dataset into an image with enhanced optical sectioning and resolution. Our method exploits the information inherently contained in the dataset to reject out-of-focus contributions and reconstruct an image with a smaller pixel size and a better resolution. The proposed method requires minimal user inputs and outperforms existing reconstruction methods.
Alessandro Zunino,Giacomo Garrè,Francesco Fersini, andGiuseppe Vicidomini
"Unlocking the full power of image scanning microscopy with maximum likelihood reconstruction", Proc. SPIE PC12848, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXXI, PC128480B (13 March 2024); https://doi.org/10.1117/12.2692249
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Alessandro Zunino, Giacomo Garrè, Francesco Fersini, Giuseppe Vicidomini, "Unlocking the full power of image scanning microscopy with maximum likelihood reconstruction," Proc. SPIE PC12848, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXXI, PC128480B (13 March 2024); https://doi.org/10.1117/12.2692249