MA3111: MATHEMATICAL IMAGE PROCESSING, Fall 2024

　

　

Instructor: Prof. Suh-Yuh Yang (楊肅煜)

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

　

Teaching Assistant: 廖育暄, E-mail: yuhsuan2023@g.ncu.edu.tw

　

Prerequisites: MA1018/MA2030/MA2044, and some knowledge of programming language Matlab

　

Textbook: No textbook but 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 course is concerned with the mathematical study of image processing. Its two objectives are

  • to introduce basic concepts and engineering approaches applicable to digital image processing and develop a further study foundation.

  • to provide some mathematical techniques for studying several fundamental questions in image processing, such as how to restore a degraded image and how to segment it into meaningful regions.

　

General Information: This course will cover the following topics

  • Basic concepts of digital image processing

  • Intensity transformations and spatial filtering

  • Variational methods for image denoising

  • Variational methods for image deblurring

  • Multi-focus image fusion and guided filter

  • Image dehazing: atmospheric scattering model and dark channel prior

  • Variational methods for image contrast enhancement

  • Image inpainting: variational methods and sparse representation

  • Variational image segmentation: Mumford-Shah and Chan-Vese models

  • Principal component pursuit problems

Assignments: will be assigned approximately every two weeks and announced at ee-class. The students are encouraged to discuss homework with other classmates. Direct copying is absolutely not allowed.

 

Course Transparency Set: (in PDF)

Grading Policy: assignments 40%, midterm 30%, and final 30% (學期總成績)

 Last updated: September 07, 2024