Lect. 
Date 
Topic 
Link 
Details 
1 
Tues 1.12 
Introduction 
Slides 
Course Policies and Introduction to Image Processing

2 
Thur 1.14 
2D ContinousSpace Signals & Systems
Basic concepts and properties 
Notes
Slides 
signal transformations
signal classes (symmetry, periodic)
2D impulse function / properties
2D systems, inputoutput relationship
system classifications
point spread function (PSF);
impulse response and representation
superposition property
convolution &
convolution properties

3 
Tues 1.19 
2D ContinousSpace Signals & Systems
Systems and Fouries Transforms 
Notes
Slides 
system properties in terms of PSF
orthogonal representation of signals
2D Fourier series
2D Fourier transform: Properties of 2D FT ;New properties
OTF/MTF
2DFT Examples
rms bandwidth, rms time duration
timebandwidth product, gaussian example

4 
Thur 1.21 
Sampling
2D DiscreteSpace Signals & Systems;
Basic concepts and properties I 
Notes
Notes
Slides 
rectilinear sampling;
nyquist sampling rate; sampling theorem;
aliasing
sinc interpolation;
video interpolation
2DDSFT / 2DFT relationship;
2D DFT/FFT, relationships to 2DFT
fftshift
2D Kronecker impulse function / properties
notation, coordinate systems; 
5 
Tues 1.26 
2D DiscreteSpace Signals & Systems;
Basic concepts and properties I

Notes
Slides
Lim
Ch. 1 
signal classes (symmetry, periodic, separable);
circular symmetry
2D systems, inputoutput relationship
system classifications;
impulse response
convolution sum; graphical convolution; separable convolution; edge effects; convolution properties
correlation; system properties in terms of PSF: causality, memory, stability, invertibility, rotation invariance;
eigenfunctions 
6 
Thur 1.28 
2D DiscreteSpace Signals & Systems;
Basic concepts and properties II
Optical imaging basics

Notes
Lim
Ch. 1
Notes
Slides 
2D discretespace Fourier transform;
existence/convergence of FT; periodic signals; properties
LSI systems in frequency domain
magnitude vs phase;
phaseonly reconstruction / magnitude retrieval;
sampling revisited
filters; introduction to filter design; separability vs rotation invariance.
Fourier transforming property of lenses
Imaging property of lenses (PSF, frequency response) 
7 
Tue 2.2 
FIR filters
Discrete Fourier Transform; DF series 
Notes
Lim
Ch. 4
Notes
Slides 
FIR filters
zero phase;
symmetries; computation
filter specs; ideal filter impulse response; filtering using FFT
Discretespace orthogonal representation; properties; Discrete Fourier series; discretespace orthogonal representation properties;
circular convolution; circular convolution example

8 
Thur 2.4 
Discrete Fourier Transform (DFT) 
Notes
Slides 
DFT; 3 imperfect perspectives: orthonormal basis, periodization, sampling DSFT; derivation; properties; circular convolution;
relationship to DSFT;
zero padding; overlapadd method; sampling dsft;
sampling H(omega) example;
matrix representation of DFT DCT: motivation; derivation; properties
FFT: row column decomposition

9 
Tues 2.9 
Image enhancement (I)

Notes
Corrections
Slides 
Contrast adjustment; piecewise linear contrast adjustment; Noise smoothing; linear vs median filters; Edge detection;
gradientbased methods; derivatives from discrete images; laplacianbased methods;

10 
Thur 2.11 
Image enhancement (II) 
Notes
Slides 
parametric edgedetection methods; canny's method; Harris corner detector;

11 
Tues 2.16 
Image enhancement (III) 
Notes
Slides 
Blob detectors and SIFT/DOG detector; statistical approach; histograms;histogram normalization; 
12 
Thur 2.18 
Motion estimation 
Notes
Slides 
Motion estimation; Motioncompensated interpolation; region matching methods; spacetime constraint equation; normal equation and optical flow 
13 
Tues 2.23 
Interpolation 
Notes
Notes on Splines
Slides 
Image interpolation; nearest, sinc, bilinear
interpolator properties; bsplines; Bsplines; Bspline interpolation; cardinal Bsplines; temporal interpolation


Tues 2.23 
Mid Term Exam 

48 hours take home exam  see eecs556.html for details;
Due on Thursday Feb 25 at 7:30pm by prof Savarese's office 
14 
Thur 2.25 
Spectral estimation (I) 
Notes
Corrections
Slides 
Random vectors; random processes, WSS;
autocorrelation, crosscorrelation; power spectral density;RP's through LSI systems; 
 
Tues 3.2 
Study Spring Break 


 
Thur 3.4 
Study Spring Break 


15 
Tues 3.9 
Spectral estimation (II) 
Notes
Slides 
Noncausal Wiener filter; Spectral estimation; periodogram; crosscorrelation and windowing; nonnegativity of power spectra;autocorrelation functions for filtered point process 
16 
Thur 3.11 
Introduction to Image restoration (I) 
Notes
Slides 
Overview; type of noise; noise estimation; degradation estimation; motion blur; Frequency domain methods;
blur removal; inverse form; Wiener filter form; examples 
17 
Tues 3.16 
No Class  ECCV Deadline 


18 
Thur 3.18 
Introduction to Image restoration (II) 
Notes
Slides 
Pixel Domain Methods; Matrix Formulation; Least Square solution; SVD; Regularization 
19 
Tues 3.23 
Introduction to Image restoration (III)
Statistical methods for image restoration (I) 
Notes
Slides 
Regularization; Tikhonov matrix; Space‐Varying Approaches; Adaptive wiener Filtering; Steepest Descent Algorithm;
Statistical methods for image restoration; ML estimation; MAP estimation 
20 
Thur 3.25 
Statistical methods for image restoration (II) 
Notes
Slides 
Statistical methods for image restoration; Penalizedlikelihood estimation; Nonquadratic regularization;
Edge preserving methods;
Huber Potentials; Examples; 
21 
Tues 3.30 
Statistical methods for image restoration (III) 
Notes
Slides 
Statistical methods for image restoration; Circulant Analysis; Circulant analysis for the QPWLS; 
22 
Thur 4.1 
Statistical methods for image restoration (IV) 
Notes
Notes
Slides 
Statistical methods for image restoration; Adaptative Wiener Filters; Other formulations; blind deconvolution; 
23 
Tues 4.6 
Image coding (I) 
Notes
Slides 
Introduction to image coding; Basic scheme; Scalar and Vector quantization; Codeword assignments 
24 
Thur 4.8 
Image segmentation 
Slides 
Image segmentation; Kmean clustering; Meanshift; normalized cuts; Other methods. 
25 
Tues 4.13 
Image coding (II)

Notes
Notes
Slides 
Predictive Coding; Transform image coding; KarhunenLoeve Transform; DCT transform; JPG compression;


Thur 4.15 
Project Presentations 1:303pm 



Tues 4.20 
Project Presentations 1:303pm 



Wed 4.21 
Project Presentations 7:008:45pm 




Project final report due: Apr 28 Midnight 

