Potential quality improvement of stochastic optical localization nanoscopy images obtained by frame by frame localization algorithms

TL;DR

This paper analyzes the potential for improving stochastic optical localization nanoscopy images by exploiting temporal correlations in frame-by-frame localization algorithms, leading to enhanced accuracy and image quality.

Contribution

It provides a theoretical analysis of how temporal correlations can reduce localization errors and suggests new algorithm development directions.

Findings

RMSMD and RMSE can be reduced by up to the square root of the average activations per emitter.

Statistical properties of localization errors are characterized and related to data frame size.

Numerical examples confirm the theoretical predictions.

Abstract

A data movie of stochastic optical localization nanoscopy contains spatial and temporal correlations, both providing information of emitter locations. The majority of localization algorithms in the literature estimate emitter locations by frame-by-frame localization (FFL), which exploit only the spatial correlation and leave the temporal correlation into the FFL nanoscopy images. The temporal correlation contained in the FFL images, if exploited, can improve the localization accuracy and the image quality. In this paper, we analyze the properties of the FFL images in terms of root mean square minimum distance (RMSMD) and root mean square error (RMSE). It is shown that RMSMD and RMSE can be potentially reduced by a maximum fold equal to the square root of the average number of activations per emitter. Analyzed and revealed are also several statistical properties of RMSMD and RMSE and their relationship with respect to a large number of data frames, bias and variance of localization errors, small localization errors, sample drift, and the worst FFL image. Numerical examples are taken and the results confirm the prediction of analysis. The ideas about how to develop an algorithm to exploit the temporal correlation of FFL images are also briefly discussed. The results suggest development of two kinds of localization algorithms: the algorithms that can exploit the temporal correlation of FFL images and the unbiased localization algorithms.