EE 264 - Image Processing and Reconstruction

Lecture Notes:

1. Overview
2. Review
3. Image Formation, Sampling, and Resolution
4. Point-wise Operations
   1. Retinex
5. Local Operations in the Image Domain
6. 2-D Signals and Systems, Matrix Formulations
7. The Frequency Domain
8. Filtering in the Freq. Domain, Sampling.
9. Some additional notes on the frequency domain
10. Restoration I: Description and basics of freq. domain approach
11. Restoration II: Power Spectra and the Wiener Filter
12. Restoration III: Basics of Pixel-domain Restoration and Statistical Methods
13. Restoration IV: Advanced Pixel Domain Restoration, Implementation
14. Motion Estimation I: Intro, models and block-based matching
15. Motion Estimation II: Optical Flow motion estimation
16. Multiresolution Image Processing
17. Introduction to Compression
18. Indirect Imaging: Tomography
19. Introduction to Image Analysis

 Homeworks:

• Homework 1: due Thursday October 9 (Solutions)(imgfilter, InterpBilinear)
  • Text problems 2.7, 2.19, 3.10, 3.28,
  • MATLAB projects 02-04, 03-05, 03-06
• Homework 2: due Tuesday October 21 (Solutions)
  • Text problems 3.24, 3.26, 4.10, 4.14, 4.17, 4.22
  • MATLAB projects 4-03, 4-04
• Homework 3: due Tuesday October 28 (Solutions)(Matlab 5.03, Matlab5.04)
  • Text problems 4.19, 5.17, 5.21, 5.26
  • MATLAB projects 5-03, 5-04

 Term Project:

• Projects will be on a topic of your choosing, with my approval
  • Project teams of 2 or 3 students
  • Two-page project proposal
    • Due Thursday October 23
    • Submit electronically or post to the web.
    • PDF File only.
• Here are some project ideas
• Project deliverables:
  • Written report approximately 10-15 pages long. PDF format. (Example)
  • 15-20 minute oral presentation. PPT/PDF format. (Example)
  • Implementation of project ideas in computer code: MATLAB format in Zip Archive. (Example)

Course Announcements and Handouts:

• Histogram equalization demo.
• Signals and systems demos from Johns Hopkins
• Retinex Image Processing
• MATLAB convolution and Fourier transform demo
• Fourier transform of line demo
• Resolution Chart
• Aliasing in images Java demo
• High-pass, low-pass, median-filtering demo
• Discrete Cosine Transform demo
• Frequency domain filtering demo
• Regularization demo m-file
• Truncated SVD image reconstruction
• Image processing demos from Chalmers Univ. in Sweden
• Optimal thresholding and Morphology Demo
• Motion Estimation demo
• m-file for Radon transform demo.
• Book on Computed Tomography
• m-file for filtered backprojection demo.
• m-file for iterative solution of linear system demo.
• m-file for image-based implementation of Steepest Descent for deconvolution.

Tentative Syllabus and Reading: 

• Week 1 (Ch. 1)

  Introduction, overview of history and applications of Image Processing. Review of the requisite mathematical concepts, including concepts in matrix, probability, and statistical analysis.

• Weeks 2 (Ch. 2)

  Fundamentals of Image Processing. Visual Perception, light and the EM spectrum. Image sensing and acquisition. Sampling and quantization.

• Weeks 3/4 (Ch. 3)

  Spatial domain image enhancement. Gray-level transforms, histogram procesing, averaging and subtraction of images. Retinex. Spatial Filtering: Convolution and superposition. Smoothing/sharpening filters.

• Week 5 (Ch. 4)

  Spectral domain enhancement. Linear transformations of images. Fourier, DCT, etc. Smoothing/sharpening filters. Homomorphic filtering.

• Week 6/7 (Ch. 5)

  Image Restoration. Modeling the degradation process. Noise and interfering patterns. Linear, space-invariant degradations. Blind restoration/estimation of the degradation process. Inverse filtering. Wiener filter, Least-squares filter. Geometric transfromations.

• Week 7 (Ch. 7)

  Wavelets and Multiresolution processing. Image pyramids, Continuous and discrete wavelet transforms. Wavelets in 2 dimensions. Applications to compression, enhancement, restoration.

• Week 8 (Ch. 9/10)

  Basic Image Analysis. Detection of discontinuities and change in images, segmentation and thresholding. Basic morphological processing. Description and respresentation of shape.

• Week 9 (Notes)

  Advanced Topics. Multiple-image processing. Image registration. Motion estimation. Resolution enhancement. Inverse Problems in imaging.

• Week 10 (Notes)

  Review of material, presentation of final projects.