29th June 2020: 'Generic Deconvolution' Rainer Heintzmann,

Duration: 1 hour 31 mins

Share this media item:

Embed this media item:

<iframe width="_width_" height="_height_" src="https://sms.cam.ac.uk/media/3253423/embed" frameborder="0" scrolling="no" allowfullscreen></iframe>

Choose size:

About this item

Available Formats

About this item

29th June 2020: 'Generic Deconvolution' Rainer Heintzmann, 's image

Description:	29th June 2020: 'Generic Deconvolution' Rainer Heintzmann, IPC, Friedrich-Schiller-Universität & Leibniz Institute of Photonic Technology, Jena, Germany


Created:	2020-07-16 18:12
Collection:	Imaging ONE WORLD
Publisher:	University of Cambridge
Copyright:	Rainer Heintzmann
Language:	eng (English)
Distribution:	World (not downloadable)
Keywords:	microscopy; Deconvolution;
Explicit content:	No
Aspect Ratio:	16:9
Screencast:	No
Bumper:	UCS Default
Trailer:	UCS Default


Abstract:	Did you ever wonder who would be the Feynmans and Einsteins of today? Our next #ImagingONEWORLD talk by @HeintzmannLab Generic Deconvolution Rainer Heintzmann, Leibniz-Institute of Photonic Technology, Jena, Germany Inst. of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany Many scientific questions can be stated as being “inverse problems”. This means that we measure some data but our interest really lies in understanding, confirming or rejecting a model having some model parameters. Most scientist are using spread-sheet tools to “fit” the parameters of a simple model (such as a linear dependence or an exponential growth) to the measured data. However, some problem require to determine millions of unknowns. Deconvolution is such a problem. The measured data may be an acquired confocal image stack and the million unknown are the emission intensities of the sample prior to being blurred by imaging process in the microscope and subjected to noise by the stochastic nature of photons and the detector noise. Maximum-Likelihood deconvolution refers to a procedure for performing such a fit, yielding the de-blurred image. In this talk, I will give an introduction to Maximum Likelihood deconvolution with practical hints and application examples. https://nanoimaging.de/ Professor Dr. Rainer Heintzmann, studied at the Universities of Osnabrück and Heidelberg and worked as a postdoctoral fellow at the Max Planck Institute of Biophysical Chemistry in Göttingen and as a group at the Randall Division, King’s College London. He is currently professor of physical chemistry at the Friedrich-Schiller University Jena, and heads the microscopy research unit at the Leibniz Institute of Photonic Technology in Jena, Germany. His research focuses on methods for imaging cellular function at high resolution and developing techniques to measure multidimensional information in small biological objects such as cells, cellular organelles or other small structures of interest. Examples of his developments are structured illumination, image inversion interferometry, optical photon reassignment, and pointillism. He is highly interested is in computer-based reconstruction and inverse modelling methods such as deconvolution.

Abstract:

Did you ever wonder who would be the Feynmans and Einsteins of today?

Our next #ImagingONEWORLD talk by @HeintzmannLab

Generic Deconvolution

Rainer Heintzmann, Leibniz-Institute of Photonic Technology, Jena, Germany
Inst. of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Germany

Many scientific questions can be stated as being “inverse problems”. This means that we measure some data but our interest really lies in understanding, confirming or rejecting a model having some model parameters. Most scientist are using spread-sheet tools to “fit” the parameters of a simple model (such as a linear dependence or an exponential growth) to the measured data. However, some problem require to determine millions of unknowns. Deconvolution is such a problem. The measured data may be an acquired confocal image stack and the million unknown are the emission intensities of the sample prior to being blurred by imaging process in the microscope and subjected to noise by the stochastic nature of photons and the detector noise. Maximum-Likelihood deconvolution refers to a procedure for performing such a fit, yielding the de-blurred image.
In this talk, I will give an introduction to Maximum Likelihood deconvolution with practical hints and application examples.

https://nanoimaging.de/

Professor Dr. Rainer Heintzmann, studied at the Universities of Osnabrück and Heidelberg and worked as a postdoctoral fellow at the Max Planck Institute of Biophysical Chemistry in Göttingen and as a group at the Randall Division, King’s College London. He is currently professor of physical chemistry at the Friedrich-Schiller University Jena, and heads the microscopy research unit at the Leibniz Institute of Photonic Technology in Jena, Germany.
His research focuses on methods for imaging cellular function at high resolution and developing techniques to measure multidimensional information in small biological objects such as cells, cellular organelles or other small structures of interest. Examples of his developments are structured illumination, image inversion interferometry, optical photon reassignment, and pointillism. He is highly interested is in computer-based reconstruction and inverse modelling methods such as deconvolution.

Available Formats

Format	Quality	Bitrate	Size
MPEG-4 Video	1280x720	2.61 Mbits/sec	1.74 GB	View
MPEG-4 Video	640x360	837.93 kbits/sec	558.48 MB	View
WebM	1280x720	1.45 Mbits/sec	996.07 MB	View
WebM	640x360	385.74 kbits/sec	257.10 MB	View
MP3	44100 Hz	250.31 kbits/sec	166.84 MB	Listen
Auto *	(Allows browser to choose a format it supports)

Streaming Media Service Upload

29th June 2020: 'Generic Deconvolution' Rainer Heintzmann,