Course Description:
This course covers the investigation, creation and manipulation of digital images by computer. The course consists of theoretical material introducing the mathematics of images and imaging. Topics include representation of two-dimensional data, time and frequency domain representations, filtering and enhancement, the Fourier transform, convolution, interpolation. The student will become familiar with Image Enhancement, Image Restoration, Image Compression, Morphological Image Processing, Image Segmentation, Representation and Description, and Object Recognition.
Course Objectives:
The objective of this course is to make students able to:
- develop a theoretical foundation of Digital Image Processing concepts.
- provide mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression.
- gain experience and practical techniques to write programs for digital manipulation of images; image acquisition; pre-processing; segmentation; Fourier domain processing; and compression.
Course Contents:
Unit 1: Introduction (5 Hrs.)
Digital Image, A Simple Image Model
Definition of digital image, pixels, representation of digital image in spatial domain as well as in matrix form.
Fundamental steps in Image Processing
Block diagram of fundamentals steps in digital image processing, application of digital image processing system, Elements of Digital Image Processing systems
Element of visual perception
Structure of the Human, Image Formation in the Eye, Brightness Adaptation and Discrimination
Sampling and Quantization
Basic Concepts in Sampling and Quantization, Representing Digital Images, Spatial and Gray- Level Resolution
Some basic relationships like Neighbors
Neighbors of a Pixel, Adjacency, Connectivity, Regions, and Boundaries, Distance Measures between pixels
Unit 2: Image Enhancement and Filter in Spatial Domain (8 Hrs.)
Basic Gray Level Transformations
Point operations, Contrast stretching, clipping and thresholding, digital negative, intensity level slicing, log transformation, power log transformation, bit plane slicing
Histogram Processing
Unnormalized and Normalized Histogram, Histogram Equalization, Use of Histogram Statistics for Image Enhancement
Spatial operations
Basics of Spatial Filtering, Linear filters, Spatial Low pass smoothing filters, Averaging, Weighted Averaging, Non-Linear filters, Median filter, Maximum and Minimum filters, High pass sharpening filters, High boost filter, high frequency emphasis filter, Gradient based filters, Robert Cross Gradient Operators, Prewitt filters, Sobel filters, Second Derivative filters, Laplacian filters
Magnification
Magnification by replication and interpolation
Unit 3: Image Enhancement in the Frequency Domain (8 Hrs.)
Introduction
Introduction to Fourier Transform and the frequency Domain, 1-D and 2-D Continuous Fourier transform, 1-D and 2-D Discrete Fourier transform
Properties of Fourier Transform
Logarthmic, Separability, Translation, Periodicity, Implications of Periodicity and symmetry
Smoothing Frequency Domain Filters
Ideal Low Pass Filter, Butterworth Low Pass Filter, Gaussian Low Pass Filter
Sharpening Frequency Domain Filters
Ideal High Pass Filter, Butterworth High Pass Filter, Gaussian High Pass Filter, Laplacian Filter
Fast Fourier Transform
Computing and Visualizing the 2D DFT (Time Complexity of DFT), Derivation of 1-D Fast Fourier Transform, Time Complexity of FFT, Concept of Convolution, Correlation and Padding.
Other Image Transforms
Hadamard transform, Haar transform and Discrete Cosine transform
Unit 4: Image Restoration and Compression (8 Hrs.)
Image Restoration
Introduction, Models for Image degradation and restoration process, Noise Models (Gaussian, Rayleigh, Erlang, Exponential, Uniform and Impulse), Estimation of Noise Parameters
Restoration Filters
Mean Filters: Arithmetic, Geometric, Harmonic and Contraharmonic Mean Filters Order Statistics Filters: Median, Min and Max, Midpoint and Alpha trimmed mean filters Band pass and Band Reject filters: Ideal, Butterworth and Gaussian Band pass and Band Reject filters
Image Compression
Introduction, Definition of Compression Ratio, Relative Data Redundancy, Average Length of Code Redundancies in Image: Coding Redundancy (Huffman Coding), Interpixel Redundancy (Run Length Coding) and Psychovisual Redundancy (4- bit Improved Gray Scale Coding: IGS Coding Scheme)
Image compression models:
Lossless and Lossy Predictive Model (Block Diagram and Explanation)
Unit 5: Introduction to Morphological Image Processing (8 Hrs.)
Introduction
Logic Operations involving binary images, Introduction to Morphological Image Processing, Definition of Fit and Hit
Morphological Operations
Dilation and Erosion, Opening and Closing
Unit 6: Image Segmentation (8 Hrs.)
Introduction
Definition, Similarity and Discontinuity based techniques
Discontinuity Based Techniques
Point Detection, Line Detection, Edge Detection using Gradient and Laplacian Filters, Mexican Hat Filters, Edge Linking and Boundary Detection, Hough Transform
Similarity based techniques
Thresholding: Global, Local and Adaptive Region Based Segmentation: Region Growing Algorithm, Region Split and Merge Algorithm
Unit 7: Representations, Description and Recognition (5 Hrs.)
Representation and Descriptions
Introduction to some descriptors: Chain codes, Signatures, Shape Numbers, Fourier Descriptors
Recognition
Patterns and pattern classes, Decision-Theoretic Methods, Introduction to Neural Networks and Neural Network based Image Recognition
Pattern Recognition
Overview of Pattern Recognition with block diagram
Laboratory Work:
Students are required to develop programs in related topics using suitable programming languages such as MatLab or Python or other similar programming languages.
Text Books:
- Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Pearson Edition, Latest Edition.
Reference Books:
- I. Pitas, “Digital Image Processing Algorithms”, Prentice Hall, Latest Edition.
- A. K. Jain, “Fundamental of Digital Image processing”, Prentice Hall of India Pvt. Ltd., Latest Edition.
- K. Castlemann, “Digital image processing”, Prentice Hall of India Pvt. Ltd., Latest Edition.
- P. Monique and M. Dekker, “Fundamentals of Pattern recognition”, Latest Edition.