University of Tsukuba | Grad. Scho. Syst. and Info. Eng. | Dept. Comp. Sci. | List of Courses
Taizo Suzuki
E-Mail taizo(at_no_spam)
Office hours Please contact Suzuki via email.
Cource# 01CH509
Area Media Engineering
Course style lecture
Term SprB
Period Mon 5,6
Room# 3A306
Keywords Signal processing, image processing, filtering, sparsity and energy minimization problem
Prerequisites There are no prerequisite lecture.
However, it is necessary to have some understanding of high school mathematics (differential integration, matrix operations, etc.).
relation degree program competence Knowledge Utilization Skills,Research Skills,Expert Knowledge
Outline Image processing by filtering, which is a multimedia technology, is presented. In particular, image denoising and smoothing with several mean filters, image edge extraction and sharpening with several differential filters, and similar image processing with sparsity and energy minimization problems are described. In order to understand each principle, including mathematical methods used as parts and devices that improve performance, this lecture explains from the basic concept to higher performance filtering while showing the actual processing results.
Course plan 1st week: Filtering
 Importance of image processing, Filtering, Kind of filters
2nd week: Image Denoising and Smoothing
 Average filter, Median filter, Birateral filter
3rd week: Image Edge Extraction and Sharpening
 Differential filter, Sobel filter, Laplacian filter
4th week: Sparsity and Energy Minimization Problem
 Convex function, Sparsity, Total variation method
5th week: Other Image Processing Methods
 Contents dependent on year
Textbook Not specified. Class slides will be uploaded on manaba.
References (1) Computer Vision -Expanding Element Technology and Application-, Kyoritsu Shuppan Co., Ltd. (2018).
(2) Digital Image Processing [newly-revised edition], CG-ARTS Society (2015).
(3) J.-L. Starck, F. Murtagh, and J. M. Fadili, Sparse Image and Signal Processing: Wavelets, Curvelets, Morphological Diversity. Cambridge University Press, (2010).
(4) R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall (2007).
Evaluation Grade is evaluated based on the overall score of the paper test (fill-in-the-blank + writing problems).
90 pt or more: A+
80-89 pt: A
70-79 pt: B
60-69 pt: C
59 pt or less: D