Lecture Notes
Imaging
Optics and Computational Imaging
Intern. Centre for Theoretical PHysics Winter
College on Optics in Imaging Science, Trieste, Italy,
Jan. 30-Febr.12, 2011
Advanced Image Processing Lab
Getting
started
Lab.
1. Image Digitization: Discretization
Lab.
2. Image Digitization: Quantization
Lab.
3. Image Coding: Predictive Methods
Lab.
4. Image Coding: Transform Methods
Lab.
5. Image Global and Local Statistics
Lab.
6. Statistical Image and Noise Models and Noise Diagnostics
Lab.
7. Image Resampling and Geometrical Transformations
Lab.
8. Target Location and Object Detection: Localization Accuracy and Reliability
Lab.
9. Target Location and Object Detection in Clutter Images
Lab.
10. Linear Filters for Image Restoration and Enhancement
Lab.
11. Rank Filters for Image Restoration, Enhancement and Segmentation
Lab.
12. Image Perfecting and Enhancement
12 Lectures
on Selected Topics in Image Processing
Synopsis
|
1 |
4.11.2008
@TB216 |
Lect.
1. Convolution integral and digital filters
|
|
2 |
7.11.2008
@TC163 |
Lect.
2. Fourier integral and Discrete Fourier Transforms
|
|
3 |
11.11.2008
@TB216 |
Lect.
3. Perfect resampling filter
|
|
4 |
14.11.2008
@TC163 |
Lect.
4. Implementations and applications of the Perfect Discrete Resampling
Filters |
|
5 |
18.11.2008
@TB216 |
Lect.
5. Precise numerical integration and differentiation
|
|
6 |
21.11.2008
@TC163 |
Lect.
6. Discrete sampling theorem
|
|
7 |
25.11.2008
@TB216 |
Lect.
7. Algorithms for reconstruction of signals from sparse samples and
applications |
|
8 |
28.11.2008
@TC163 |
|
|
9 |
2.12.2008
@TB216 |
Lect.
9. Accuracy and reliability of target location
|
|
10 |
5.12.2008
@TC163 |
Lect.
10. Target location in clutter
|
|
11 |
9.12.2008
@TB216 |
Lect.
11. Local adaptive transform domain scalar filters
|
|
12 |
12.12.2008
@TC163 |
Introduction to Digital
Holography
Synopsis
Lecture plan: Spring 2009
|
# |
Date |
Subject |
|
1 |
Febr. 10 |
|
|
2 |
Febr. 17 |
|
|
3 |
Febr. 24 |
Lect.
3 Digital Recording and Numerical Reconstruction of Holograms |
|
4 |
March 3 |
|
|
5 |
March 10 |
Lect.
5. Methods for Encoding CGH for Recording on Physical Media |
|
6 |
March 17 |
|
|
7 |
March 24 |
|
|
8 |
March 31 |
|
|
9 |
Apr. 7 |
Lect.
9. Lect. 9. Stochastic Noise Models in Imaging and Digital
Holography |
|
10 |
Apr. 14 |
Computer-Generated Holograms and 3-D Visual Communication: a Tutorial
3DTV-CONFERENCE
2008
28-30 MAY 2008, ISTANBUL, TURKEY
Tutorial, EUSIPCO2000,
Advanced Image
Processing Lab.:
Lecture 1. Signal Fast Sinc-interpolation
Lecture
2. Statistical Noise models and Diagnostics
Lecture 3.
Image Restoration, Enhancement and Segmentation: Linear Filters
Lecture 4. Image Restoration, Enhancement and
Segmentation: Nonlinear Filters
Lecture 5. Image Parameter Estimation
Lecture 6.
Target Location in Clutter
From
Photography to *-Graphies: Unconventional Imaging Techniques
Lecture 1.
Evolution of Imaging: Direct Image Plane Imaging
Lecture
2. Evolution of Imaging: Transform Imaging
Lecture 3. Principles of Fourier Optics
Lecture 4. Principles of Reconstructive
Tomography
Lecture 5. Discrete Representation of Imaging
Transforms
Lecture 6. Sinc-interpolation in Digital Imaging
Lecture 7. Speckle Noise in Coherent Imaging Systems
Lecture 8. Methods and Means for Recording Computer
Generated Holograms
Holography and Microscopy
Selected Topics in Advanced Digital Signal
and Image Processing
Tampere
Lect.
1. Optical transforms in digital holography
Lect.
3. Numerical integration and differentiation of sampled data
Lect.
4. Spatial, temporal and inter-channel data fusion for image restoration and
enhancement in long distance observation systems
Lect.
5. Redundancy of stereoscopic vision and new methods for 3-D stereoscopic image
display
Archive-TAU 1995-2008
Selected Topics in Image
Processing
|
|
|
|
|
|
|
Lecture 1. Imaging
transforms
|
(2 hours) |
|
Lecture 2. Imaging
transforms in digital computers |
(6 hours) |
|
Lecture 3. Fast
transform methods for image resampling |
(6 hours) |
|
Lecture 4. Efficient
computational algorithms for digital image processing |
(2 hours) |
|
Lecture 5. Image
data fusion |
(4 hours) |
|
Lecture 6. Digital
holographic imaging |
(6 hours) |
|
Lecture 7. 3-D
imaging and vision |
(2 hours) |
Digital Image Processing:
Applications
Lecture
plan-2008
Introduction
Lecture 1. Principles of Image Digitization
Lecture
2. Image Discretization
Lecture 3. Image Quantization in Image and Transform
Domains
Lecture 4. Image Coding Methods
Lecture
5. Statistical Image and Noise Models
Lecture
6. Imaging Transforms
Lecture
7. Discrete Representation of Imaging Transforms
Lecture 8. Methods of Image Filtering and Resampling in
Signal and Transform Domains
Lecture
9. Image Quantification: Localization, Registration, Recognition
Lecture
10. Target Location in Clutter
Lecture 11. Image Restoration and Enhancement: Linear
Filters
Lecture
12. Image Restoration, Enhancement and Segmentation: Nonlinear
Filters
Lecture 13. Methods of Image Perfecting and
Enhancement
Biomedical Signal and Image
Processing
Lecture
1. Signals and mathematical models
Lecture
2 Digital representation of signals
Lecture
3 Signal discretization by sampling
Lecture
4 Element-wise quantization
Lecture
5 Principles of signal and image coding
Lecture
6 Signal transformations and their discrete representation. Digital filters
Lecture
7 Discrete representations of Fourier Transform
Lecture 8 Applications of DFT and SDFTs
Lecture
9 Principles of signal parameter estimation
Lecture
10 Signal reconstruction and enhancement: linear filters
Lecture
11Signal/image restoration: nonlinear filters
Lecture 12 Correlational averaging as a method for
signal restoration
Lecture 13 Ultrasound image processing for
quantitative analysis of fetal movement
Test
signals and images for exercises
Fundamentals of Image
Processing
Introduction
Lecture
1. Imaging devices
Lecture
2. Elements of the theory of 2D signal processing
Lecture
3. Signal transformations and mathematical models of imaging systems
Lecture
4. Principles of signal digitization. Signal sampling
Lecture
5. Image quantization
Lecture
6. Principles of image coding
Lecture
7. Digital representation of signal transformations
Properties
of DFT
Lecture
8. Orthogonal Transforms in Digital Image Processing
Lecture
9. Statistical Image and Noise Models
Lecture
10. Principles of Image Restoration Lecture
11. Image enhancement
Exercise
1. Introduction to Matlab.
Exercise
2. Signal sampling
Exercise
3. Image quantization
Exercise
4. Image coding
Exercise
5. Digital convolution ; Digital
convolution demo
Exercise
6. Discrete Fourier Transforms