Web: http://arxiv.org/abs/2209.07911

Sept. 19, 2022, 1:14 a.m. | Kira Vinogradova, Eugene W. Myers

cs.CV updates on arXiv.org arxiv.org

The quality of microscopy images often suffers from optical aberrations.
These aberrations and their associated point spread functions have to be
quantitatively estimated to restore aberrated images. The recent
state-of-the-art method PhaseNet, based on a convolutional neural network, can
quantify aberrations accurately but is limited to images of point light
sources, e.g. fluorescent beads. In this research, we describe an extension of
PhaseNet enabling its use on 3D images of biological samples. To this end, our
method incorporates object-specific information …

arxiv

More from arxiv.org / cs.CV updates on arXiv.org

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Tech Business Data Analyst

@ Fivesky | Alpharetta, GA

Senior Applied Scientist

@ Amazon.com | London, England, GBR

AI Researcher (Junior/Mid-level)

@ Charles River Analytics Inc. | Cambridge, MA

Data Engineer - Machine Learning & AI

@ Calabrio | Minneapolis, Minnesota, United States