Learning objectives
The main goal of the course is endowing the students with the basic knowledge of digital image processing with a special attention to images taken from laboratory equipments, such as microscopes
Prerequisites
- - -
Course unit content
Introduction to the technology of digital image representation and processing.
Full programme
Introduction
Image formation basis
Electronic devices for image acquisition
Color and image representation
Image digital representation and storage
Digital representation of an image
Digital acquisition of an image
Binary representation
Pixel matrix
Image matrix models
Grayscale
RGB
Indexed
Digital format for image transmission and storage
The problem behind data storage and transmission
Common image data formats
TIFF
GIF
PNG
JPEG : principle and problems
Introduction to the language of Octave/Matlab
Computing arithmetic expressions with Octave
Variables creation and assignment
Matrices, Arrays and Scalars
Operations between scalars, matrices and arrays
Matrix composition
Application of library functions to arrays and matrices
Plotting
2-D plot
Multiple plots within the same diagram
3-D plot
Matrices and Images in Matlab/Octave
Matrix and data structures representing grayscale, RGB and indexed
images
Classes of data representation
Integers numbers
Floating Point numbers
Scripting
Code execution from m-files
Functions
Execution flow control structures
Contitionals
Loops
Image Package
Some basic functions within the Image Processing package
iminfo
imwrite
imread
imshow
Example of display of a matrix representing a grayscale
and RGB image
Intensity Transformation
Basic manipulation
RGB to grayscale conversion: rgb2gray
Color luminosity equivalence formula
rgb2ind
Histogram
Image histogram computation: imhist
Histogram-image relationship
Overexposed/Underexposed typical histograms
Contrast
Dynamics
Point Operation
Luminosity inversion
Contrast and luminosity modification
Logarithmic and Exponention transformation
Thresholding
Automatic contrast transform
Automatic contrast transform (improved method)
Histogram equalization
Histogram modification accordingly to a reference histogram
Filters
Linear Filters
Smoothing filters
Average nearest neighbors filter
Smoothing effect on a noise corrupt image
Difference filters
Filter as a convolution
Identity Filter (δ)
Optical system point spread function determination
Non linear filters
Max and min filter
Median filter
Example of application of a median filter
Edge Enhancement
Border detection
Border characteristics
Border as a derivative of a image function
Gradient: border strength and direction
Derivative filters
Prewitt and Sobel matrices
Roberts filter
Canny method
Function 'edge' of Octave image package
Edge Sharpening
Genral strategy of Edge Sharpening
Laplace operator effect on a test image
Laplace operator approach to edge sharpening
Unsharp Masking
Morphological Filters
Structuring elements
Erosion
Dilation
Effects of combined applications
Opening
Closing
Generalization to grayscale images
Application to egde detection
Bibliography
W. Burger - Mark J. Burge
Digital Image Processing (Springer)
R.Gonzalez, R.Woods
Digital Image Processing (Pearson)
Chris Solomon - Toby Breckon
Fundamentals of Digital Image Processing (Wiley-Blackwell)
Teaching methods
About half of the course develops in classroom lectures and half in laboratory sessions
Assessment methods and criteria
Oral exam
Other information
the teacher is maintaining the website http://imaging.biol.unipr.it/ where teaching material and exercises are available
