Image Processing in Optical Nanoscopy (IPNano)5 ECTS
(englische Bezeichnung: Image Processing in Optical Nanoscopy)

Lehrveranstaltungen:

• • Image Processing in Optical Nanoscopy
    (Vorlesung mit Übung, Harald Köstler et al., Mo, 12:15 - 13:45, 00.151-113; nächster Termin ist der 2.5.16, date for excursion to OICE is on xxx)

Inhalt:

The module includes two interlinked topics. First, an introduction to the techniques of optical imaging (e.g. for biological specimen) with a special focus on recently evolving super-resolution techniques beyond the diffraction barrier. Second, the students will be given an overview of existing numerical techniques in imaging processing especially for image deblurring. The focus lies on algorithms based on FFT, energy minimization, and sparse coding. Additionally one makes use of information about the imaging system. The algorithms are applied to optical imaging and implemented in Matlab.

Lernziele und Kompetenzen:

After participation in the module Supervised Teaching, students are able to autonomously plan and perform a lesson or exercise with a given content. They know the most important principles of good didactics and rhetoric und can implement them in the course. Furthermore, the participants are able to reflect their teaching on their own and together with students and colleagues.

Literatur:

The relevant scientific literature is the one of the corresponding introductory courses. For didactics:
A. Winteler, Professionell lehren und lernen - Ein Praxisbuch für Universität und Schule, Wissenschaftliche Buchgesellschaft, 2004
Arthur Mattuck, The Torch or The Firehose: A Guide to Section Teaching, MIT OpenCourseWare

Organisatorisches:

Interessenten melden sich per Mail bei harald.koestler@informatik.uni-erlangen.de

Weitere Informationen:

Studien-/Prüfungsleistungen:

Image Processing in Optical Nanoscopy (Prüfungsnummer: 534357)

(englischer Titel: Image Processing in Optical Nanoscopy)

Prüfungsleistung, mündliche Prüfung, Dauer (in Minuten): 30, benotet, 2.5 ECTS

Anteil an der Berechnung der Modulnote: 50.0 %

Erstablegung: SS 2016, 1. Wdh.: WS 2016/2017

1. Prüfer:

Harald Köstler