Dekonvolution von Mikroskopiedaten bei niedrigem Signal-Rausch-Verhältnis

Abstract Fluorescence live cell microscopy is central to the analysis of inter- and intracellular signaling. However, highly dynamic, local processes requires high temporal spatial resolution imaging, which intrinsically linked a low signal-to-noise ratio. To improve image quality after data acquisition, computational techniques, referred as deconvolution, are being developed. Here, we discuss recent approaches in areas variational deep learning deconvolution.