Super-resolution radial fluctuations (SRRF) is a combination of temporal fluctuation analysis and localization microscopy. One of the key differences between SRRF and other super-resolution methods is its applicability to live-cell dynamics because it functions across a very wide range of fluorophore densities and excitation powers. SRRF is applied to data from imaging modes which include widefield, TIRF and confocal, where short frame bursts (e.g. 50 frames) can be processed to deliver spatial resolution enhancements similar to or better than structured illumination microscopy (SIM). On the other hand, with sparse data e.g. stochastic optical reconstruction microscopy (STORM), SRRF can deliver resolution similar to Gaussian fitting localization methods. Thus, SRRF could provide a route to super-resolution without the need for specialized optical hardware, exotic probes or very high-power densities. We present a fast GPUbased SRRF algorithm termed "SRRF-Stream" and apply it to imagery from an iXon EMCCD coupled to a multi-modal imaging platform, Dragonfly. The new implementation is >300 times faster than the standard CPU version running on an Intel Xeon 3.5GHz 4 core processor, and > 20 times faster than the NanoJ GPU implementation, while also being integrated with acquisition for real time use. In this paper we explore the image resolution and quality with EMCCD and sCMOS cameras and various fluorophores including fluorescent proteins and organic dyes.
An automated unsupervised technique, based upon a Bayesian framework, for the segmentation of low light level imagery is proposed. Primarily, Mixture Modelling is used to provide a baseline estimate. This estimate is then refined to consider spatial correlations using Markov Random Field (MRF) Modelling. The technique has been implemented assuming low-light level Poisson statistics, and the results compared to the more widely used assumption of Gaussian statistics. Investigations revealed the Poisson technique quantitatively outperforms the Gaussian technique for synthetic low light imagery, both before and after avalanche multiplication, via the multiplication register of a Low-light Level Charge Coupled Device (L3CCD). The technique was then applied to the task of segmenting a biomedical dataset obtained from a L3CCD. Qualitative results were promising, again showing improvement over the Gaussian technique.
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