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Stimulated emission depletion (STED) microscopy provides subdiffraction resolution while preserving useful aspects of fluorescence microscopy, such as optical sectioning, and molecular specificity and sensitivity. However, sophisticated microscopy architectures and high illumination intensities have limited STED microscopy's widespread use in the past. Here we summarize the progress that is mitigating these problems and giving substantial momentum to STED microscopy applications. We discuss the future of this method in regard to spatiotemporal limits, live-cell imaging and combination with spectroscopy. Advances in these areas may elevate STED microscopy to a standard method for imaging in the life sciences.

Image deblurring is an important topic in imaging science. In this review, we consider together fluorescence microscopy and optical/infrared astronomy because of two common features: in both cases the imaging system can be described, with a sufficiently good approximation, by a convolution operator, whose kernel is the so-called point-spread function (PSF); moreover, the data are affected by photon noise, described by a Poisson process. This statistical property of the noise, that is common also to emission tomography, is the basis of maximum likelihood and Bayesian approaches introduced in the mid eighties. From then on, a huge amount of literature has been produced on these topics. This review is a tutorial and a review of a relevant part of this literature, including some of our previous contributions. We discuss the mathematical modeling of the process of image formation and detection, and we introduce the so-called Bayesian paradigm that provides the basis of the statistical treatment of the problem. Next, we describe and discuss the most frequently used algorithms as well as other approaches based on a different description of the Poisson noise. We conclude with a review of other topics related to image deblurring such as boundary effect correction, space-variant PSFs, super-resolution, blind deconvolution and multiple-image deconvolution.