Course Title: Image Processing
Course No: BIT456
Nature of the Course: Theory + Lab
Semester: VIII
https://www.studysathi.com/2024/08/bit-eight-semester-image-processing.html
Course Description:
This course covers the investigation, creation and manipulation of digitalimages 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, ImageRestoration, Image Compression, Morphological Image Processing, Image Segmentation, Representation and Description, and Object Recognition.
Course Objectives:
To enhance a theoretical foundation of Digital Image Processing concepts, To provide mathematical foundations for digital manipulation of images; image acquisition; preprocessing; segmentation; Fourier domain processing; and compression, To make able to gain experience and practical techniques to write programs for digital manipulation ofimages; image acquisition; pre-processing; segmentation; Fourier domain processing; and compression.
- To provide a strong theoretical foundation in Digital Image Processing.
- To offer mathematical foundations for digital image manipulation, acquisition, preprocessing, segmentation, Fourier domain processing, and compression.
- To give students hands-on experience in writing programs for digital image manipulation, acquisition, preprocessing, segmentation, Fourier domain processing, and compression.
Course Contents:
Unit 1: Introduction and Fundamentals (5 Hrs.)
Definition of digital image, pixels, representation of digital image in spatial domain as well as in matrix form, Block diagram of fundamentals steps in digitalimage processing, Elements of Digital Image Processing systems, Light and EM Spectrum, Image acquisition using a single sensor, Image Acquisition Using Sensor Strips, Image Acquisition process, A simple image formation model, Representing Digital Images, Spatial and Intensity Resolution, Image Interpolation, Neighbors of a Pixel, Adjacency, Connectivity,Regions, and Boundaries
- Definition of digital images and pixels
- Representation in spatial domain and matrix form
- Block diagram of fundamental steps in digital image processing
- Elements of Digital Image Processing systems
- Light, EM Spectrum, and image acquisition techniques
- Simple image formation model, spatial and intensity resolution, interpolation
- Pixel neighborhoods, adjacency, connectivity, regions, and boundaries
Unit 2: Intensity Transformations and Spatial Filtering (8 Hrs.)
Spatial domain, Transform domain, Spatial Domain Process, Image Negatives, Log Transformations, Power-Law (Gamma) Transformations, Bit-plane Slicing, Histogram Equalization, Histogram Matching, Basics of Spatial Filtering, Spatial Correlation, Spatial Convolution, 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
- Spatial and transform domains
- Image negatives, logarithmic transformations, power-law transformations
- Bit-plane slicing, histogram equalization, and matching
- Basics of spatial filtering: linear and non-linear filters, smoothing and sharpening filters, gradient-based filters
Unit 3: Filtering in the Frequency Domain (8 Hrs.)
Fourier Series and Fourier Transform, Impulses and the Sifting Property, The Discrete Fourier Transform (DFT) of One Variable, 2-D Fourier Transform, Aliasing in Images, Moiré patterns, Properties of the 2-D DFT, Zero Padding, Zero-Phase-Shift Filters, Image Smoothing Using Filter Domain Filters, Image Sharpening Using Frequency Domain Filters, 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, Hadamard transform, Haar transform and DiscreteCosine transform
- Fourier series and transforms, Discrete Fourier Transform (DFT)
- Moiré patterns, zero padding, and phase shift filters
- Smoothing and sharpening using frequency domain filters
- Fast Fourier Transform (FFT), convolution, correlation, padding
- Hadamard, Haar, and discrete cosine transforms
Unit 4: Image Restoration & Reconstruction (8 Hrs.)
A Model of Image Degradation/Restoration Process , Noise Sources , Range Imaging, Noise Models, Mean Filters: Arithmetic, Geometric, Harmonicand 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 BandReject filters, Introduction, Definition of Compression Ratio, Relative Data Redundancy, Average Length ofCode, 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 degradation/restoration models, noise sources, and noise models
- Mean and order statistics filters, bandpass/reject filters
- Image compression: redundancy, coding techniques like Huffman and Run Length Coding
Unit 5: Introductionto Morphological Image Processing (2 Hrs.)
Logic Operations involving binary images, Introduction to Morphological Image Processing, Definition of Fit and Hit, Dilation and Erosion, Opening and Closing
- Binary image operations, dilation, erosion, opening, and closing
Unit 6 Image Segmentation (8 Hrs.)
Definition, Similarity and Discontinuity Based Techniques, Point Detection, Line Detection, Edge Detection Using Gradient and Laplacian Filters; Mexican Hat Filters, Edge Linking and Boundary Detection, Hough Transform; Thresholding: Global, Local and Adaptive; Region Based Segmentation: Region Growing Algorithm, Region Split and Merge Algorithm
- Edge detection, line detection, point detection
- Techniques like gradient, Laplacian, Mexican Hat Filters, Hough transform
- Thresholding and region-based segmentation methods
Unit 7 Wavelet Transform (2 Hrs.)
Fourier vs. Wavelet, Shifting, Five Steps to a Continuous Wavelet Transform, Coefficient Plots, Wavelet synthesis
- Differences between Fourier and wavelet transforms
- Continuous Wavelet Transform and synthesis
Laboratory Works:
Students are required to develop programs in related topics using suitable programminglanguages such as Python or other similar programming languages.
References:
1. Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing”, Pearson Edition,Latest Edition.
2. I. Pitas, "Digital Image Processing Algorithms", Prentice Hall, Latest Edition.
3. A. K. Jain, “Fundamental of Digital Image processing”, Prentice Hall of India Pvt. Ltd.,Latest Edition.
4. K. Castlemann, “Digital image processing”, Prentice Hall of India Pvt. Ltd., Latest Edition.
5. P. Monique and M. Dekker, “Fundamentals of Pattern recognition”, Latest Edition.