Image Processing

Image Processing focuses on the analysis and manipulation of digital images to extract useful information. It covers image enhancement, filtering, segmentation, feature extraction, and basic computer vision techniques used in areas such as medical imaging, surveillance, and multimedia applications.

Image Processing

Course Title: Image Processing

Course No: BIT456

Nature of the Course: Theory + Lab

Semester: 8

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

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.

Course Objectives

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 Contents

1. Introduction and Fundamentals

5 hrs

1.1. Definition of digital image, pixels, spatial domain vs matrix form

1.2. Fundamentals steps, Elements of DIP systems

1.3. EM Spectrum, Image acquisition, simple image formation model

1.4. Resolution, Interpolation

1.5. Adjacency, Connectivity

2. Intensity Transformations and Spatial Filtering

8 hrs

2.1. Spatial vs Transform domain

2.2. Negatives, Log, Power-Law Transformations, Bit-plane Slicing

2.3. Histogram Equalization/Matching

2.4. Basics of Spatial Filtering, Correlation/Convolution

2.5. Linear filters

Low pass, Averaging

2.6. Non-Linear filters

Median, Max, Min

2.7. High pass sharpening filters, Gradient based filters

3. Filtering in the Frequency Domain

8 hrs

3.1. Fourier Series/Transform, DFT

3.2. 2-D Fourier Transform, Aliasing/Moiré

3.3. Properties of 2-D DFT, Zero Padding

3.4. Smoothing/Sharpening Frequency Filters

3.5. Time Complexity (DFT vs FFT)

3.6. Hadamard, Haar, and Discrete Cosine transform

4. Image Restoration and Reconstruction

8 hrs

4.1. Degradation/Restoration Process Model, Noise Sources and Models

4.2. Mean Filters, Order Statistics Filters

4.3. Band pass and Band Reject filters

4.4. Compression Ratio, Redundancy

Coding: Huffman; Interpixel: RLE; Psychovisual: IGS

5. Introduction to Morphological Image Processing

2 hrs

5.1. Logic Operations, Fit and Hit definitions

5.2. Dilation, Erosion, Opening, Closing

6. Image Segmentation

8 hrs

6.2. Point/Line/Edge Detection

Gradient, Laplacian, Mexican Hat

6.3. Hough Transform

6.4. Thresholding

Global, Local, Adaptive

6.5. Region Growing, Region Split and Merge

7. Wavelet Transform

2 hrs

7.1. Fourier vs. Wavelet

7.2. Five Steps to CWT, Coefficient Plots

7.3. Wavelet synthesis

Text Books

1.Rafael C. Gonzalez and Richard E. Woods, "Digital Image Processing", Pearson Edition.
2.I. Pitas, "Digital Image Processing Algorithms", Prentice Hall.
3.A. K. Jain, "Fundamental of Digital Image processing", Prentice Hall of India Pvt. Ltd.

Notes:

This syllabus follows the official BIT curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.

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