Digital Image Processing

Course Title: Digital Image Processing

Course No: CSIT.415.4

Nature of the Course: Theory + Lab

Semester: 7

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 10 + 10

Credit Hours: 3

Course Description

The course will cover techniques and tools for digital image processing, and finally also introduce image analysis techniques in the form of image segmentation. The course is primarily meant to develop on-hand experience in applying these tools to process these images. The students would be encouraged to develop the image processing tools from scratch, rather than using any image processing library functions.

Course Objectives

Develop an overview of the field of image processing. Understand the fundamental algorithms and how to implement them. Prepare to read the current image processing research literature. Gain experience in applying image processing algorithms to real problems.

Course Contents

1. Introduction

4 hrs

1.1. Light and Image Basics

Light

Brightness adaption and discrimination

Pixels

Coordinate conventions

Imaging

1.2. Image Geometry and Sampling

Geometry

Perspective Projection

Spatial Domain Filtering

Sampling and quantization

2. Image Filtering

10 hrs

2.1. Spatial Domain Filtering

Intensity transformations

Contrast stretching

Histogram equalization

Correlation and convolution

Smoothing filters

Sharpening filters

Gradient and Laplacian

2.2. Frequency Domain Filtering

Hotelling Transform

Fourier Transforms and properties

FFT (Decimation in Frequency and Decimation in Time Techniques)

Convolution

Correlation

2-D sampling

Discrete Cosine Transform

Frequency domain filtering

3. Image Restoration

6 hrs

3.1. Restoration Framework and Transformations

Basic Framework

Interactive Restoration

Image deformation and geometric transformations

Image morphing

Restoration techniques

3.2. Noise and Degradation

Noise characterization

Noise restoration filters

Adaptive filters

Linear, Position invariant degradations

Estimation of Degradation functions

Restoration from projections

4. Image Compression

13 hrs

4.1. Compression Fundamentals

Encoder-Decoder model

Types of redundancies

Lossy and Lossless compression

Entropy of an information source

Shannon's 1st Theorem

4.2. Coding Techniques

Huffman Coding

Arithmetic Coding

Golomb Coding

LZW coding

Transform Coding

4.3. Transform Coding Implementation

Sub-image size selection

Blocking artifacts

DCT implementation using FFT

4.4. Image Coding Standards

Run length coding

FAX compression (CCITT Group-3 and Group-4)

Symbol-based coding

JBIG-2

Bit-plane encoding

Bit-allocation

Zonal Coding

Threshold Coding

JPEG

Lossless predictive coding

Lossy predictive coding

Motion Compensation

4.5. Wavelet-based Compression

Expansion of functions

Multi-resolution analysis

Scaling functions

MRA refinement equation

Wavelet series expansion

Discrete Wavelet Transform (DWT)

Continuous Wavelet Transform

Fast Wavelet Transform

2-D wavelet Transform

JPEG-2000 encoding

Digital Image Watermarking

5. Image Processing

6 hrs

5.1. Morphological Operations

Basics

Erosion

Dilation

Opening

Closing

Hit-or-Miss Transform

Boundary Detection

Hole filling

5.2. Morphological Algorithms

Connected components

Convex hull

Thinning

Thickening

Skeletons

Pruning

Geodesic Dilation

Erosion

Reconstruction by dilation and erosion

6. Image Segmentation

6 hrs

6.1. Boundary Detection

Boundary detection based techniques

Point detection

Line detection

Edge detection

Edge linking

Local processing

Regional processing

Hough transform

6.2. Thresholding

Thresholding

Iterative Thresholding

Otsu's method

Moving averages

Multivariable Thresholding

6.3. Region-based Segmentation

Region based segmentation

Watershed algorithm

Use of motion in segmentation

Laboratory Works

Student should implement different algorithms discussed in class by using programming language of interest and prepare to make a lab sheet. At least 15 algorithms needs to be implemented.

1.Algorithm Implementation

Text Books

1.Digital Image Processing, 3rd Edition, by Rafael C Gonzalez and Richard E Woods. Publisher: Pearson Education.

Reference Books

1.N. Efford, Digital Image Processing, Addison Wesley 2000
2.M Sonka, V Hlavac and R Boyle, Image Processing, Analysis and Machine Vision, PWS 1999
3.W K Pratt, Digital Image Processing, John Wiley and Sons, 1991

Notes:

This syllabus follows the official CSIT curriculum of Far Western University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative. https://cdc.fwu.edu.np/faculties.html

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TabFlux . Digital Image Processing . FWU . BSc. CSIT

Digital Image Processing

Course Description

Course Objectives

Course Contents

Laboratory Works

Text Books

Reference Books

Notes: