Henry Pinkard, Zachary Phillips, Arman Babakhani, Daniel Fletcher and Laura Waller
EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2019-161
December 1, 2019
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-161.pdf
Maintaining an in-focus image over long time scales is an essential and non-trivial task for a vari- ety of microscopy applications. Here, we describe a fast and robust auto-focusing method that is compatible with a wide range of existing micro- scopes. It requires only the addition of one or a few off-axis illumination sources (e.g. LEDs), and can predict the focus correction from a single im- age with this illumination. We designed a neural network architecture, the fully connected Fourier neural network (FCFNN), that exploits an under- standing of the physics of the illumination in or- der to make accurate predictions with 2-3 orders of magnitude fewer learned parameters and less memory usage than existing state-of-the-art ar- chitectures, allowing it to be trained without any specialized hardware. We provide an open-source implementation of our method, in order to enable fast and inexpensive autofocus compatible with a variety of microscopes.
Advisor: Laura Waller
BibTeX citation:
@mastersthesis{Pinkard:EECS-2019-161, Author = {Pinkard, Henry and Phillips, Zachary and Babakhani, Arman and Fletcher, Daniel and Waller, Laura}, Title = {Deep learning for single-shot autofocus microscopy}, School = {EECS Department, University of California, Berkeley}, Year = {2019}, Month = {Dec}, URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-161.html}, Number = {UCB/EECS-2019-161}, Abstract = {Maintaining an in-focus image over long time scales is an essential and non-trivial task for a vari- ety of microscopy applications. Here, we describe a fast and robust auto-focusing method that is compatible with a wide range of existing micro- scopes. It requires only the addition of one or a few off-axis illumination sources (e.g. LEDs), and can predict the focus correction from a single im- age with this illumination. We designed a neural network architecture, the fully connected Fourier neural network (FCFNN), that exploits an under- standing of the physics of the illumination in or- der to make accurate predictions with 2-3 orders of magnitude fewer learned parameters and less memory usage than existing state-of-the-art ar- chitectures, allowing it to be trained without any specialized hardware. We provide an open-source implementation of our method, in order to enable fast and inexpensive autofocus compatible with a variety of microscopes.} }
EndNote citation:
%0 Thesis %A Pinkard, Henry %A Phillips, Zachary %A Babakhani, Arman %A Fletcher, Daniel %A Waller, Laura %T Deep learning for single-shot autofocus microscopy %I EECS Department, University of California, Berkeley %D 2019 %8 December 1 %@ UCB/EECS-2019-161 %U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-161.html %F Pinkard:EECS-2019-161