Course Title: Digital Image Processing
Course No. : CSE 4105
Contact hours/week: 3
Credits: 3.00
Prerequisite: None
Course Description
Digital Image Fundamentals: Different types of digital images, sampling and quantization, imaging geometry, image acquisition systems.
Bilevel Image Processing: Basic concepts of digital distances, distance transform, medial axis transform, component labeling, thinning, morphological processing, extension to grey scale morphology.
Binarization of Grey level images: Histogram of grey level images, optimal thresholding using Bayesian classification, multilevel thresholding.
Detection of edges : First order and second order edge operators, multi-scale edge detection, Canny's edge detection algorithm, Hough transform for detecting lines and curves, edge linking.
Images Enhancement: Point processing, Spatial Filtering, Frequency domain filtering, multi-spectral image enhancement, image restoration.
Image Segmentation: Segmentation of grey level images, Water shade algorithm for segmenting grey level image. Image representation and description, recognition and interpretation.
Image compression: Lossy and lossless compression schemes, prediction based compression schemes, vector quantization, sub-band encoding schemes, JPEG compression standard, Fractal compression scheme, Wavelet compression scheme.
Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab/Python/other appropriate digital image processing software.
Grading
Quizzes/Class Test: 20 Marks* (3 best out of 4 quizzes/class tests may be taken for awarding grade)
Homework’s/Attendance: 8 Marks*
Semester Exam: 72 Marks
* - We reserve the right to change the above grading scheme.
Homework’s
A new homework is released and is then due after 9 days. No grade will be given to homework submitted afterwards. Homework solutions should be written and submitted individually, but discussions among students are encouraged.
Referred Books
Digital Image Processing, Rafael C. Gonzalez and Richard E. Woods.
Digital Image Processing, William K. Pratt.
Digital Image Processing, Kenneth R. Castleman.
Others
Notice
Class Outline
Handouts
Course Details:
Digital Image Fundamentals:
Introduction
Introduction
Digital Image
Digital Image Processing
Fundamental Steps in Digital Image Processing
Image Aquisition
Image Enhancement
Image Restoration
Morphological Processing
Segmentation
Representation and Description
Object Recognition
Components of an Image Processing System
Elements of Visual Perception
Brightness Adaptation and Discrimination
Image Sensing and Acquisition
Image Sensing and Acquisition
Single Sensor
Sensor Strip (Linear)
Sensor Array
A Simple Image formation Model
Image Sampling and Quantization
Basic Concepts in Sampling and Quantization
Representing Digital Image
Image Sampling and Quantization
Spatial and Intensity Resolution
Image Interpolation
Nearest Neighbor Interpolation
Bilinear Interpolation
Bicubic Interpolation
Some Basic Relationships between Pixels
Neighbors of a Pixel
Adjacency, Connectivity, Regions, and Boundaries
Distance Measures
Mathematical Tools Used in Digital Image Processing
Array versus Matrix Operation
Linear versus Nonlinear Operation
Arithmetic Operation
Mathematical Tools Used in Digital Image Processing
Set and Logical Operations
Spatial Operations
Mathematical Tools Used in Digital Image Processing
Vector and Matrix Operations
Image Transforms
Probabilistic Methods
Image Enhancement:
Image Enhancement
(Intensity Transformation and Spatial Filtering )
Background
The Basics of Intensity Transformations and Spatial Filtering
Some Basic Intensity Transformation Functions
Image Negatives
Log Transformations
Power-Law(Gamma) Transformations
Piecewise-Linear Transformation Functions
Image Enhancement
(Intensity Transformation and Spatial Filtering )
Histogram Processing
Histogram Equalization
Histogram Matching(Specification)
Local Histogram processing
Using Histogram Statistics for image enhancement
Image Enhancement
(Intensity Transformation and Spatial Filtering )
Fundamentals of Spatial Filtering
The Mechanics of Spatial Filtering
Spatial Correlation and Convolution
Vector Representation of Linear Filtering
Generating Spatial Filter Masks
Smoothing Spatial Filters
Smoothing Linear Filters
Order-Statistic (Nonlinear) Filters
Image Enhancement
(Intensity Transformation and Spatial Filtering )
Sharpening Spatial Filters
Foundation
Using the Second Derivative for Image SharpeningThe Laplacian
Unsharp Masking and Highboost Filtering
Using First-Order Derivatives for (Nonlinear) Image Sharpening The Gradient
Combining Spatial Enhancement Methods
Image Enhancement
(Filtering in the Frequency Domain)
Background
Preliminary Concepts
Complex Numbers
Fourier Series
Even and Odd Function
Impulses and Their Sifting Property
The Fourier Transform of Functions of One Continuous Variable
Convolution
Sampling and the Fourier Transform of Sampled Functions
Sampling
The Fourier Transform of Sampled Functions
The Sampling Theorem
Aliasing
Function Reconstruction (Recovery) from Sampled Data
The Discrete Fourier Transform (DFT) of One Variable
Obtaining the DFT from the Continuous Transform of a Sampled Function
Relationship Between the Sampling and Frequency Intervals
Image Enhancement
(Filtering in the Frequency Domain)
Extension to Functions of Two Variables
The 2-D Impulse and Its Sifting Property
The 2-D Continuous Fourier Transform Pair
Two-Dimensional Sampling and the 2-D Sampling Theorem
Aliasing in Images
The 2-D Discrete Fourier Transform and Its Inverse
Some Properties of the 2-D Discrete Fourier Transform
Relationships Between Spatial and Frequency Intervals
Translation and Rotation
Periodicity
Symmetry Properties
Fourier Spectrum and Phase Angle
The 2-D Convolution Theorem
Summary of 2-D Discrete Fourier Transform Properties
Image Enhancement
(Filtering in the Frequency Domain)
The Basics of Filtering in the Frequency Domain
Additional Characteristics of the Frequency Domain
Frequency Domain Filtering Fundamentals
Summary of Steps for Filtering in the Frequency Domain
Correspondence Between Filtering in the Spatial and Frequency Domains
Image Smoothing Using Frequency Domain Filters
Ideal Lowpass Filters
Butterworth Lowpass Filters
Gaussian Lowpass Filters
Additional Examples of Lowpass Filtering
Image Enhancement
(Filtering in the Frequency Domain)
Image Sharpening Using Frequency Domain Filters
Ideal Highpass Filters
Butterworth Highpass Filters
Gaussian Highpass Filters
The Laplacian in the Frequency Domain
Unsharp Masking, Highboost Filtering, and
High-Frequency-Emphasis Filtering
Homomorphic Filtering
Selective Filtering
Bandreject and Bandpass Filters
Notch Filters
Implementation
Separability of the 2-D DFT
Computing the IDFT Using a DFT Algorithm
The Fast Fourier Transform (FFT)
Course Title: Sessional Based on CSE 4105
Course No. : CSE 4106
Contact hours/week: 1.5
Credits: 0.75
Prerequisite: None
Course Description
Sessional based on the theory of course CSE 4105.
Emphasis is on the general principles of image processing. Students learn to apply material by implementing and investigating image processing algorithms in Matlab/Python/other appropriate digital image processing software.
Grading
Quiz Test: 20 Marks*
Homeworks/Attendance: 8 Marks*
Board Viva: 25
Others: 47 Marks*
* - We reserve the right to change the above grading scheme.
Referred Books
Digital Image Processing Using Matlab, Rafael C. Gonzalez and Richard E. Woods.
Others
Notice
Course Outline
Handouts
Module01.pdf
Module02.pdf
Module03.pdf
Module04.pdf