2 code implementations • 14 Feb 2021 • Adrian Shajkofci, Michael Liebling
We estimated the velocity vector field from the local estimation of the blur model parameters using an deep neural network and achieved a prediction with a regression coefficient of 0. 92 between the ground truth simulated vector field and the output of the network.
1 code implementation • 8 Oct 2020 • Adrian Shajkofci, Michael Liebling
Following microscope-specific calibration, we further demonstrate that the recovered PSF model parameters permit estimating surface depth with a precision of 2 micrometers and over an extended range when using engineered PSFs.
no code implementations • 8 Oct 2020 • Adrian Shajkofci, Michael Liebling
In microscopy, the time burden and cost of acquiring and annotating large datasets that many deep learning models take as a prerequisite, often appears to make these methods impractical.
1 code implementation • 2 Jan 2020 • Adrian Shajkofci, Michael Liebling
Autofocus (AF) methods are extensively used in biomicroscopy, for example to acquire timelapses, where the imaged objects tend to drift out of focus.
2 code implementations • 20 Mar 2018 • Adrian Shajkofci, Michael Liebling
We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm.