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EE225b - Digital Image Processing
Instructor |
TA |
Vito Dai |
Visual data is becoming increasingly more commonplace in today's world. For one thing, a large amount of traffic on the internet and large amount of data on the web is of visual nature. For another, the advent of consumer electronics products such as digital cameras and camcorders has resulted in the explosion of the amount of visual data, we as consumers, generate, store, process and manipulate.
In this course, we will deal with fundamental aspects of image representation, processing, enhancement, restoration, compression, coding and communication. We begin with fundamentals of multidimensional digital signal processing and then move on to describe techniques and methodologies for image/video processing.
Announcements
Class on Fri 2/21 is cancelled, there is a make-up lecture Mon 2/24, 4-5:30pm, Homework/Lab 3 is due in class on Monday
Pre-makeup lecture Mon 3/10 at 11-12:30
No class Wed 3/19
Lectures
Lectures for EE225b are recorded for viewing in VHS and via webcast. In addition, the lecture notes used in class are posted on this web page in electronic pdf format.
Lecture 1 - Introduction. One-dimensional vs. multi-dimensional signal processing.
Lecture 2 - One-dimensional vs. multi-dimensional signal processing. Properties of the Fourier transform.
Lecture 3 - Tomography and the projection slice theorem
Lecture 4 - Reconstructing a signal from the Fourier transform magnitude
Lecture 5 - Reconstructing a signal from the Fourier transform phase
Lecture 6 - 2D Z-transform, IIR filters, and recursive computability
Lecture 7 - Recursive computability and boundary conditions
Lecture 8 - Boundary condition for 2D-LSI systems. 2D-DFT
Lecture 9 - Computing the 2D-DFT
Lecture 10 - Computing the 2D-DFT, I/O operations. Introduction to 2D filter design
Lecture 11 - 2D filter design using window method and frequency sampling
Lecture 12 - 2D filter design using transformation
Lecture 13 - 2D filter design, designing the transformation
Lecture 14 - Introduction to images and image processing
Lecture 15 - Color systems
Lecture 16 - Image Sensing and Acquisition
Lecture 17 - Image Enhancement
Lecture 18 - Edge Detection
Lecture 19 - Image restoration from images corrupted by noise [Images shown in class]
Lecture 20 - Image restoration - Frequency domain techniques
Lecture 21 - Image restoration - Weiner filtering
Lecture 22 - Image restoration - Adaptive Weiner filtering, power spectrum filtering
Lecture 23 - Image restoration - Deconvolution
Lecture 24 - Image compression - Introduction, Huffman, Arithmetic, and LZ77
Lecture 25 - Image restoration - Blind deconvolution, removing multiplicative noise, Image and video compression
Lecture 26 - Image compression - Quantization and Bit Assignment
Lecture 27 - Image compression - Discrete Cosine Transform
Lecture 28 - Image compression - PCM, Differential Coding, DPCM, Transform Coding
Lecture 29 - Image compression - Wavelet and Subband Coding
Lecture 30 - Class project presentations
Homework and Labs
Homework 1 - Problem 1.28, 1.30, 1.33, 1.34, 1.35 (due in class Wed 2/5)
Homework 2 - Lab: Phase-only image reconstruction (due in class Fri 2/14) [Phase.dat, Magnitude.dat, Test.bmp]
Homework 3 - Lab: Tomography (due in class Mon 2/24) [Pyramid.bmp]
Homework 4 - Problem 2.3, 2.5, 2.7, 2.18 (due in class Fri 2/28)
Homework 5 - Problem 3.20, 3.21, 3.22, 3.28 (due in class Fri 3/7)
Homework 6 - Problem 4.12, 4.14, 4.15, 4.16, 4.17, 4.20 (due in class Fri 3/21) [Turtle.bmp]
Homework 7 - Lab: Image Enhancement (due in class Fri 4/4) [Berkeley.jpg]
Homework 8 - Lab: Image Restoration (due in class Wed 4/31) [NoisyImg.bmp, NoisyBlur.bmp]
Other useful references
last updated on 05/09/2003 by Vito Dai