E E 596
Topics of current interest in signal and image processing. Content may vary from offering to offering. Prerequisite: permission of instructor.
The first part of the course provides the background and basic techniques for digital image processing. The second part of the course cover various special topics including image restoration, high dynamic range imaging, image fusion, multi-resolution with wavelet transform, image super resolution, and 3D-video.
Student learning goals
The goal of this course is to introduce to students the basic theory and recent important topics related to digital image and video processing.
Course Contents: 1. Image Representations (0.5 week) * Image acquisition, Sampling, Quantization 2. Visual Perception and Color Spaces (1 week) * Physiological characteristics of the eye and image formation * Human color vision * Color models: CIE, RGB, CMYK, HSI, HSV, L*a*b* 3. Spatial Domain Image Enhancement and Filtering (1 week) * Point processing (contrast enhancement, histogram equalization, íK) * Spatial domain 2-D LSI filtering * Median filtering 4. Frequency Domain Image Filtering and Enhancement (1 week) * 2-D Discrete Fourier Transform * Frequency domain LSI filtering * Enhancement in the frequency domain 5. Edge Detection and Mathematical Morphology (1 week) 6. Image Restoration (1 week) * Image degradation model * Inverse Filtering * Wiener filtering 7. Multi-resolution and Wavelet Transform (1.5 week) 8. High Dynamic Range Image and Image Fusion (1 week) 9. Image Super Resolution (0.5 week) 10. 3-D Video (1 week)
General method of instruction
The course consists of weekly lectures and homework. The homework exercises are specifically designed towards providing hands-on experience on digital image processing.
Prerequisites: EE 341, Discrete Time Signal Analysis, familiarity with MATLAB, or permission of the Coordinator. Textbook: Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd Ed. Prentice Hall, 2008. http://www.imageprocessingbook.com/
Class assignments and grading
There will be weekly homework and a midterm. Students also need to submit a final report on a topic related to image processing.
Midterm: 40% Homework: 30% Final report: 30%