Convex Analysis for Image Processing
Lecturer:
Dr. Simon Setzer,
Office hours: Tuesday, 14:1515:15
Winter Term 2010/2011
Lectures (3h) with exercises (1h)
(6 credit points)
Time and Location: Monday 1618 c.t. and Thursday 1618 c.t., Building E13, Lecture Hall 001
First lecture: Thursday, October 21, 2010
Announcements –
Description –
Prerequisites –
Lecture notes –
Assignments –
Exams –
Literature
Homework 6 is now online.
The exam schedule can be found here.
You can now register here for this course.
Many problems in image processing can be modeled as convex minimization problems. The restored image is thus the minimizer of a suitable convex energy functional. We will cover the foundations of convex analysis (convex sets and functions, subdifferentials, duality, optimality conditions) and their applications to nonsmooth nonlinear optimization.
Except for basic concepts of calculus and linear algebra this course is selfcontained, especially, because we will be working in the finitedimensional setting. There will also be programming assignments which illustrate the relevance of fast optimization algorithms.
Undergraduate knowledge in mathematics (e.g. ''Mathematik
für Informatiker IIII''). The lectures will be given
in English.
Homework will be assigned biweekly. To qualify for the exam you need 50% of the points from these assignments.
The first exam will be Tuesday, February 22, 2011 at 9am12pm in room E1 3, HS001. The second exam will take place Friday, March 25, 2011 (same time, same room).
Please remember that you have to register online for the lecture/exam
in the HISPOS system of the Saarland University
The lecture notes which will be provided for this course are selfcontained. No textbook is required. Examples of books giving background material and further reading are:
 Convex analysis
R. T. Rockafellar, Princeton University Press, 1997
(Classical text on convex analysis.)
 Convex analysis and minimization algorithms I+II
J.B. HiriartUrruty and C. Lemaréchal, Springer, 1993
(Provides many nice examples and motivational ideas.)
 Convex Optimization
S. Boyd and L. Vandenberghe, Cambridge University Press, 2004
(Also available to download at the authors' webpages.)
 Perturbation analysis of optimization problems
Joseph F. Bonnans and A. Shapiro, Springer, 2000
(Works in the infinitedimensional setting. Nice introduction to duality.)
 Numerical Optimization
J. Nocedal and S. J. Wright, Springer, 2006
(Introduction to a wide range of modern optimization techniques. Good motivational examples.)
 Image Processing and Analysis
T. F. Chan and J. Chen, SIAM, 2005
(Not only restricted to optimization methods in image processing but introduces major models.)
