Course Name: Digital Image Processing

 

Main Text book: Digital Image Processing (2nd Edition)

                           Authors: Rafael C.Gonzalez, Richard E.Woods

                           Publisher: Prentice Hall, 2002

 

Supporting Text Book: Digital Image Processing using MATHLAB

                                     Authors: Rafael C.Gonzalez, Richard E.Woods, Steven L.Eddins

                                     Publisher: Prentice Hall, 2004

 

Course syllabus:

 

  1. Introduction

1.1  What id Digital Image Processing

1.2  The Origins of digital Image Processing

1.3  Examples of Fields that Use Digital Image Processing

1.4  Fundamental Steps in Digital Image Processing

1.5  Components of Image Processing System

 

  1. Digital Image Fundamentals

2.1. Elements of Visual Perception

2.2 Light and the Electromagnetic Spectrum

2.3 Image Sensing and Acquisition

2.4 Image Sampling and Quantization

2.5 Some Basic Relationship Between Pixels

2.6 Linear and Non-Linear Operations

 

  1. Image Enhancement in the Spatial Domain

3.1  Background

3.2  Some Basic Gray Level Transformations

3.3  Histogram Processing

3.4  Enhancement Using Arithmetic/Logic Operation

3.5  Basics of Spatial Filtering

3.6  Smoothing Spatial Filters

3.7  Sharpening Spatial Filters

3.8  Combining Spatial Enhancement Methods

 

  1. Image Enhancement in the Frequency Domain

4.1  Background

4.2  Introduction to Fourier Transform and the Frequency Domain

4.3  Smoothing Frequency-Domain Filters

4.4  Sharpening Frequency Domain Filters

4.5  Homomorphic Filters

4.6  Implementations

4.7  Convolution and Correlation

4.8  Fast Fourier Transform

4.9  Walsh, Harr and Discrete Fourier Transform

  1. Image Restoration

5.1  A Model of the Image Degradation/Restoration Process

5.2  Noise Models

5.3  Restoration in the Presence of Noise Only-Spatial Filtering

5.4  Periodic Noise Reduction by Frequency Domain Filtering

5.5  Linear, Position-Invariant Degradation

5.6  Estimating the Degradation Function

5.7  Inverse Filtering

5.8  Minimum Mean Square Error (Wiener) Filtering

5.9  Constrained Least Squares Filtering

5.10          Geometric Mean Filter

5.11          Geometric Transformations

 

  1. Color Image Processing

6.1 Color Fundamentals

6.2 Color Models

6.3 Pseudocolor Image Processing

6.4 Basics of Full-Color Image Processing

6.5 Color Transforms

6.6 Smoothing and Sharpening

6.7 Color Segmentation

6.8 Noise in Color Images

6.9 Color Image Compression

 

 

Course Evaluation is done as follows:

1.      Mid Term Exam

2.      Final Exam

3.      Seminar (Oral presentation of a research paper by students)

4.      Course work (Implementation of some basic algorithms using MATHLAB)

5.      Course Project (Programming a research topic that may be related to the seminar)