MA3113: TOPICS IN MATHEMATICAL IMAGE PROCESSING I, Spring 2026

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Instructor: Prof. Suh-Yuh Yang (·¨µÂ·Ô)

Office Hours: Tuesday 10:00~12:00 am or by appointment

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Prerequisites: MA3111 and some knowledge of Matlab: https://portal.ncu.edu.tw/      

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Textbook: No textbook, but we will provide some slides and journal papers. Below are some references:

  • [AK2002] G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations, Second Edition, Springer Verlag, New York, 2002.

  • [CS2005] T. F. Chan and J. Shen, Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods, Society for Industrial and Applied Mathematics, Philadelphia, 2005.

  • [TUM2019] D. Cremers, Computer Vision I: Variational Methods, Online Resources, Departments of Informatics & Mathematics, Technical University of Munich, Germany, 2019/2020. https://vision.in.tum.de/teaching/online/cvvm

  • [GW2018] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Fourth Edition, Pearson Education Limited, New York, 2018.

Course Objective: This is a companion course of the¡uMA3111 An Introduction to Mathematical Image Processing.¡vWe will continue to introduce advanced mathematical techniques for image processing based on partial differential equations and variational methods. This course emphasizes practical implementation and computer simulations. In addition, every student must complete a research project on image processing and make several presentations.

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General Information: This course will cover the following topics

  • Advanced PDE and variational methods for image processing.

  • Advanced optimization methods for image processing.

  • Principal component pursuit theory with applications to image processing.

Course Transparency Set: (in PDF)

Grading Policy: oral presentations (20%)¡Ñ3, poster of project results (20%), and others 20% (¾Ç´ÁÁ`¦¨ÁZ)

 Last updated: February 16, 2026

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