Scalable Variational Inference for Super Resolution Microscopy
Ruoxi Sun, Evan Archer, Liam Paninski
Abstract
Super-resolution microscopy methods have become essential tools in biology, opening up a variety of new questions that were previously inaccessible with standard light microscopy methods. In this paper we develop new Bayesian image processing methods that extend the reach of super-resolution microscopy even further. Our method couples variational inference techniques with a data summarization based on Laplace approximation to ensure computational scalability. Our formulation makes it straightforward to incorporate prior information about the underlying sample to further improve accuracy. The proposed method obtains dramatic resolution improvements over previous methods while retaining computational tractability.
BibTeX
@InProceedings{pmlr-v54-sun17a,
title = {{Scalable variational inference for super resolution microscopy}},
author = {Sun, Ruoxi and Archer, Evan and Paninski, Liam},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {1057--1065},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/sun17a/sun17a.pdf},
url = {https://proceedings.mlr.press/v54/sun17a.html},
abstract = {Super-resolution microscopy methods have become essential tools in biology, opening up a variety of new questions that were previously inaccessible with standard light microscopy methods. In this paper we develop new Bayesian image processing methods that extend the reach of super-resolution microscopy even further. Our method couples variational inference techniques with a data summarization based on Laplace approximation to ensure computational scalability. Our formulation makes it straightforward to incorporate prior information about the underlying sample to further improve accuracy. The proposed method obtains dramatic resolution improvements over previous methods while retaining computational tractability.}
}