Digital Image Processing notes

Description:
Note #1
Module 1 Concept of Visual Information

Module 2 Perception

Module 3 Sampling

Module 4 Image Transformations

Module 5 Image Enhancement

Module 6 Restoration

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Note #2
Lecture 1:
Outline:

Image Formation
Inside the Camera - Projection
Inside the Camera - Sensitivity
Sensitivity and Color
Summary
Digital Image Formation
Sampling
Quantization
Summary
(R,G,B) Parameterization of Full Color Images
Grayscale Images
Images as Matrices
Homework I

Lecture 2:
Outline:

Summary of Lecture 1
Simple Processing - Transpose
Simple Processing - Flip Vertical
Simple Processing - Cropping
Simple Image Statistics - Sample Mean and Sample Variance
Simple Image Statistics - Histogram
Point Processing
Summary
Homework Rules
Homework II

Lecture 3:
Outline:

Summary of Lecture 2
Brief Note on Image Segmentation
Histogram Based Image Segmentation
Histogram Equalization
Summary
Homework III

Lecture 4:
Outline:

Summary of Lecture 3
Histogram Matching - Specification
Quantization
Summary
Homework IV

Lecture 5:
Outline:

Summary of Lecture 4
Designing the Reproduction Levels for Given Thresholds
MSQE Optimal Lloyd-Max Quantizer
Systems
Linear Systems
Linear Shift Invariant (LSI) Systems
Summary
Homework V

Lecture 6:
Outline:

Summary of Lecture 5
Convolution and Linear Filtering
The Fourier Transform of 2-D Sequences
Fourier Transform Types
Sampling and Aliasing
Summary
Homework VI

Lecture 7:
Outline:

Summary of Lecture 6
The Need for a ``Computable'' Fourier Transform
The 2-D DFT for Finite Extent Sequences
DFTs of Natural Images
Importance of Low Frequencies
Convolution by DFTs
Summary
Homework VII

Lecture 8:
Outline:

Summary of Lecture 7
2-D Low-Pass Filtering of Images
2-D High-Pass Filtering of Images
2-D Band-Pass Filtering of Images
Sampling and Antialiasing Filters
Noise Removal
Summary
Homework VIII

Lecture 9:
Outline:

Summary of Lecture 8
Fourier Transforms and Gibbs Phenomenon
Images and Edges
Edge Detection - Motivation
Human Visual System and Mach Bands
Summary
Homework IX

Lecture 10:
Outline:

Summary of Lecture 9
``Perceptual'' Image Processing
Quantization and False Contours
Image Halftoning
Image Warping and Special Effects
Median Filtering
Oil Painting
Homework X

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Note #3
Lect. 1. Convolution integral and digital filters



Lect. 2. Fourier integral and Discrete Fourier Transforms



Lect. 3. Perfect resampling filter




Lect. 4. Implementations and applications of the Perfect Discrete Resampling Filters



Lect. 5. Precise numerical integration and differentiation



Lect. 6. Discrete sampling theorem




Lect. 7. Algorithms for reconstruction of signals from sparse samples and applications



Lect. 8. Target location as a parameter estimation task



Lect. 9. Accuracy and reliability of target location



Lect. 10. Target location in clutter


Lect. 11. Local adaptive transform domain scalar filters



Lect. 12. Local adaptive nonlinear filters
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