EE 264: Image Processing and Reconstruction
Fundamental concepts in digital image processing and reconstruction. Continuous and discrete images, image acquisition, sampling. Linear transformations of images, convolution and superposition. Image enhancement and restoration, spatial and spectral filtering. Temporal image processing: change detection, image registration, motion estimation. Image reconstruction from incomplete data. Applications.
Instructor: 
Peyman Milanfar 
Office: 
Engineering 2 , Room 243A 
Phone: 
(831) 4594929 
email: 
milanfar@ee.ucsc.edu 


Lecture: 
T/Th 10:00 to 11:45, Baskin 156 
Office Hours: 
T/Th 12 PM or by appointment 
Required Text: 
Digital Image Processing by Gonzalez and Woods, Third Edition (Errata) 
Optional Reference Texts: 
 Digital Image Processing Using MATLAB by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins
 Digital Image Processing by William K. Pratt
 Fundamentals of Digital Image Processing by Anil K. Jain
 TwoDimensional Imaging by Ronald N. Bracewell

Grading Policy: 
Homeworks (20%), Midterm (30%), Final Project (50%) 
Notes on MATLAB: 
Homework exercises will require the use of the software package MATLAB. Here are some resources to get you started:

Important Dates:
First day of class 
Thursday, Sept. 22 
Last day of class 
Thursday, December 1 
Midterm examination 
Tuesday, November 8 
Final Project presentations 
Tuesday, December 6 
Final Project reports, presentation slides, and code due 
Tuesday, December 6 
Homeworks:
 Homework 1: due Tues Oct. 4 (Solutions)(imgfilter, spatfilt, InterpBilinear)
 Text problems 2.7, 2.19, 3.11, 3.29, 3.30
 MATLAB projects 0204, 0304, 0305
 Homework 2: due Thurs. Oct 13 (Solutions) (Matlab 4.0304)
 Text problems 3.23 , 3.27, 4.20a,c,e, 4.21, 4.22, 4.27, 4.28, 4.39
 MATLAB projects 403, 404
 Homework 3: due Thurs. Oct. 27 (Solutions)(Matlab 5.03, Matlab5.04, Example of SD reconstruction)
 Text problems 4.29, 4.30, 5.17, 5.21, 5.22, 5.26
 MATLAB projects 503, 504 (+part(e): Restore the image using Steepest Descent method)
 Project Summary: due Thurs. Nov. 3 according to the guidelines here.
Term Project:
 Projects will be on a topic from the list (link below), with my approval
 Project teams of at most 2 students
 SELECT YOUR PROJECT ASAP, but no later than the midterm date. Only one team will be able to work on each project topic.
 Project deliverables:
 Written report approximately 15 pages long. PDF format. (Example)
 1520 minute oral presentation. PPT/PDF format. (Example)
 Implementation of project ideas in computer code: MATLAB format in Zip Archive. (Example)
 Final Projects Page (UPDATED WITH REQUIRED PROJECT SUMMARY DESCRIPTION)
Links and Matlab Demos:
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. Graylevel 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, spaceinvariant degradations. Blind restoration/estimation of the degradation process. Inverse filtering. Wiener filter, Leastsquares 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. Multipleimage processing. Image registration. Motion estimation. Resolution enhancement. Inverse Problems in imaging.
 Week 10 (Notes)Review of material, presentation of final projects.
 EE 262: Statistical Signal Processing
 Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise; includes analysis of their performance. Binary hypothesis testing: the NeymanPearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Reviews the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.
 EE 264: Image Processing and Reconstruction
 Fundamental concepts in digital image processing and reconstruction. Continuous and discrete images, image acquisition, sampling. Linear transformations of images, convolution and superposition. Image enhancement and restoration, spatial and spectral filtering. Temporal image processing: change detection, image registration, motion estimation. Image reconstruction from incomplete data. Applications.
 EE 153: Digital Signal Processing
 Introduction to the principles of signal processing, including discretetime signals and systems, the ztransform, sampling of continuoustime signals, transform analysis of linear timeinvariant systems, structures for discretetime systems, the discrete Fourier transform, computation of the discrete Fourier transform, filter design techniques.
 EE 103: Signals and Systems
 Characterization and analysis of continuoustime signals and linear systems. Time domain analysis using convolution. Frequency domain analysis using the Fourier series and the Fourier transform. The Laplace transform, transfer functions and block diagrams. Continuoustime filters.
Brief Biography
Updated July 2012
Peyman Milanfar is a Professor of Electrical Engineering (Currently on leave at Google.) He was Associate Dean for research and graduate studies from 2010 to 2012. He received the B.S. in EE/Mathematics from Berkeley, and his Ph.D. in EECS from MIT. Prior to UCSC, he was at SRI, and a Consulting Professor of CS at Stanford. In 2005 he founded MotionDSP , which has brought stateofart video enhancement to market. His interests are in statistical signal, image and video processing, computational photography and vision. He is a member of the IEEE Signal Processing Society’s IVMSP Technical Committee, and its Awards Board. He is a Fellow of the IEEE.
Source:
http://classes.soe.ucsc.edu/ee264/Fall11/
http://classes.soe.ucsc.edu/ee264/
http://users.soe.ucsc.edu/~milanfar/teaching/index.html
http://users.soe.ucsc.edu/~milanfar/
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