Often, two-dimensional Gaussian function is used as an estimate of the PSF. Localization of emitters is performed by fitting an estimated point spread function (PSF) to each intensity blob, which is potentially an image of an emitter, in each frame. The exploitation occurs in the form of localizing only a few optically separable emitters in each frame and performing such localizations for several frames, each with independent and sparse set of emitters. Localization microscopy (LM) is an umbrella term referring to those super-resolution methods in fluorescence microscopy that exploit sparse spatio-temporal emissions of fluorophores (referred to as emitters for simplicity). The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images.
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