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Modulkataloge

Name des Moduls [116290] Computational Imaging Modulcode PAFMO129

Studiengang [128] Physik ECTS Punkte 4 LP

Arbeitsaufwand für Selbststudium 75 Stunden Häufigkeit des Angebotes (Modulturnus) jedes 2. Semester (ab Sommersemester)
Arbeitsaufwand in Präsenzstunden 45 Stunden Dauer des Moduls 1 Semester
Arbeitsaufwand Summe (Workload) 120 Stunden    

Modulverantwortlicher

Prof. Dr. Rainer Heintzmann, Dr. Lars Lötgering

Voraussetzungen für die Vergabe von Leistungspunkten

project presentation (100%)

Zusätzliche Informationen zum Modul

A list of Literature and materials will be provided at the beginning of the semester.

Literatur

A list of Literature and materials will be provided at the beginning of the semester.

Unterrichtssprache

Englisch

Art des Moduls

128 M.Sc. Physics focus „Optics”: Required elective module

628 M.Sc. Photonics: Required elective module

Zusammensetzung des Moduls / Lehrformen

Lecture: 2 h per week

Programming Lab: 1 h per week

Inhalte

Review: Linear Algebra, Calculus, Python

• Optimization part 1: Continuous (Euler Lagrange) and Discrete (multivariate calculus)

• Programming lab: genetic algorithms + Fermat principle

• Optimization part 2: nonlinear optimization, regularization, Lagrange multipliers

• Optimization part 3: Convex techniques, l1 minimization

• Programming lab: single pixel camera

• Optimization part 4: Automatic differentiation

• Matrix representation of coherent optical systems Programming lab: keras toolbox, optical eigenmodes

• Multiple scattering: Born / Rytov series, beam propagation method

• Tomographic inversion  

• Programming lab: Foldy-Lax scattering theory

• Phase retrieval part 1: coherent diffraction imaging (CDI)

• Phase retrieval part 2: ptychography

• Programming lab: hybrid input output, shrink wrap, ptychography

• Phase retrieval part 3: Fourier ptychography

• Image deconvolution: structured illumination microscopy, pupil engineering

• Programming lab: extended depth-of-field systems

• Imaging with spatially partially coherent light

• Parameter estimation: Fisher information and Cramer Rao lower bound

• Programming lab: Coded aperture imaging, resolution assessment, edge responses, modulation transfer function, Fourier ring correlation

• Neural networks part 1: Image classification

• Neural networks part 2: Image regression

• Programming lab: digit recognition, counting red blood cells

Lern- und Qualifikationsziele

Understanding the interplay between forward and inverse modeling in optical systems. Hands-on programming skills.




PAFMO129 ... Computational Imaging Modulhandbuch


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