Biomedical Signal and Image Processing
PDF files:
Lecture 1. Signals and mathematical
models (197 K)
Lecture 2 Digital representation of
signals (178 K)
Lecture 3 Signal discretization by
sampling (776 K)
Lecture 4 Element-wise quantization (534K)
Lecture
5 Principles of signal and image coding (565K)
Lecture
6 Signal transformations and their discrete representation. Digital filters
(152 K)
Lecture
7 Discrete representations of Fourier Transform (690 K)
Lecture 8 Applications of DFT and SDFTs (640 K)
Lecture
9 Principles of signal parameter estimation (818 K)
Lecture
10 Signal reconstruction and enhancement: linear filters (1187 K)
Lecture
11Signal/image restoration: nonlinear filters (1417 K)
Lecture 12 Correlational averaging as a method for
signal restoration (170 K)
Lecture 13 Ultrasound image processing for quantitative
analysis of fetal movement (1.200
K)
Test
signals and images for exercises (Zip-file, 489 Kb)
Fundamentals of Image
Processing
PDF files:
Introduction
Lecture
1. Imaging devices (3 Mb)
Lecture
2. Elements of the theory of 2D signal processing (50 Kb)
Lecture
3. Signal
transformations and mathematical models of imaging systems (69
Kb)
Lecture
4. Principles of signal digitization. Signal sampling (800 Kb)
Lecture
5. Image quantization (490 Kb)
Lecture
6. Principles of image coding (314 Kb)
Lecture
7. Digital
representation of signal transformations (716 Kb)
Properties
of DFT (66 KB)
Lecture
8. Orthogonal
Transforms in Digital Image Processing (450 Kb)
Exercise 1.
Introduction to Matlab. (105
Kb)
Exercise
2. Signal sampling (Zip-file,
489 Kb)
Exercise
3. Image quantization (Zip-file, 489 Kb)
Exercise
4. Image coding (Zip-file, 489 Kb)
Exercise
5. Digital convolution (Zip-file, 489 Kb); Digital
convolution demo (Ppt file 225
KB)
Exercise
6. Discrete Fourier Transforms
(Zip-file, 489
Kb)
Digital Image Processing: Applications
PDF files:
Lecture i. Introduction (825 K)
Lecture 1. Principles of Image
digitization (251 K)
Lecture 2. Signal Discretization (1.637 K)
Lecture 3. Element-wise Quantization (1.147K)
Lecture 4. Principles of Image Coding
(603 K)
Lecture 5. Signal Transformations (399
KB)
Lecture 6. Discrete Representation of Signal
Transformations (394 KB)
Lecture 7. Methods of Image Filtering in Signal and
Transform Domain (285 KB)
Lecture 8. Statistical Image and Noise models (1232
K)
Lecture 9. Image Parameter Estimation and
Recognition (274 K)
Lecture 10. Target Location in Clutter (523 K)
Lecture 11. Image Restoration and Enhancement (Linear
Filters) (804 K)
Lecture 12. Image Restoration, Enhancement and
Segmentation (Rank Filters) (455 K)
Lecture 13. Image Calibration and Enhancement
(900 K)
Lecture 14. Efficient Algorithms and Computational
Complexity (44
K)
Advanced Image Processing
Lab.:
Tutorial, EUSIPCO2000, Tampere, Finland,
Sept. 2000
PDF files:
Lecture 1. Signal Fast Sinc-interpolation
(1430 K)
Lecture 2. Statistical Noise models and
Diagnostics (344 K)
Lecture 3. Image Restoration, Enhancement and
Segmentation: Linear Filters (848 K)
Lecture 4. Image Restoration, Enhancement and
Segmentation: Nonlinear Filters (617K)
Lecture 5. Image Parameter Estimation (374 K)
Lecture 6. Target Location in Clutter (246 K)
From Photography to *-Graphies:
Unconventional Imaging Techniques,
Tampere University of
Technology, Finland, Sept.3 - 14, 2001
PDF files:
Lecture 1. Evolution of Imaging: Direct Image Plane
Imaging (3609 K)
Lecture 2. Evolution of Imaging: Transform
Imaging (1676 K)
Lecture 3. Principles of Fourier Optics (300 K)
Lecture 4. Principles of Reconstructive
Tomography (480 K)
Lecture 5. Discrete Representation of Imaging
Transforms (405 K)
Lecture 6. Sinc-interpolation in Digital Imaging
(1600 K)
Lecture 7. Speckle Noise in Coherent Imaging
Systems (1300 K)
Lecture 8. Methods and Means for Recording Computer
Generated Holograms (550 K)
Holography and
Microscopy
Tampere
University of Technology, Finland, Sept. 19, 2002 (PDF-file,
1.5 Mb)