Digital Image Processing CS-323

Digital Image Processing (CS-323)

Course Type: Core
Batch: 3rd Year CSE, B.Tech and Dual Degree
Course Credits: 04

To impart knowledge about the fundamental steps of digital image processing.
To introduce the fundamental concepts relevant to digital image processing.
To enable the students to understand the various image segmentation techniques and morphological algorithms.
To enable the students to understand the various image compression techniques.

There are no official pre-requisites for this course.

Vivekananda Lecture Hall (F5, B3 and G1)

Monday: 03:00 PM - 04:00 PM
Tuesday: 02:00 PM - 03:00 PM
Wednesday: 09:00 AM - 10:00 AM
Thursday: 10:00 AM - 11:00 AM

Introduction: Digital image representation, Fundamental steps in image processing, Elements of Digital Image processing systems, Elements of visual perception, Image model, Sampling and quantization, Relationship between pixels, Imaging geometry.
Image Enhancement: Enhancement by point processing, Intensity transformation, Histogram processing, Image subtraction, Image averaging, Spatial filtering, Smoothing filters, Sharpening filters, Frequency domain: Low-Pass, High-Pass, Homomorphic filtering.
Image Segmentation: Detection of discontinuities, Edge linking and boundary detection, Thresholding, Region oriented segmentation, Use of motion in segmentation, Spatial techniques, Frequency domain techniques.
Image Restoration: A model of the image degradation/restoration process, noise models, restoration in the presence of noise only spatial filtering, Weiner filtering, constrained least squares filtering, geometric transforms; Introduction to the Fourier transform and the frequency domain, estimating the degradation function. Color Image Processing.
Morphological Image Processing: Preliminaries, dilation, erosion, open and closing, hit or miss transformation, basic morphological algorithms.
Image Compression: Coding redundancy, Inter-pixel redundancy, fidelity criteria, image compression.

Upon successful completion of the course, the students will be able to:

CO1: Understand fundamental steps of digital image processing.
CO2: Understand and implement image enhancement techniques.
CO3: Implement and compare various image compression techniques.
CO4: Understand and implement pattern recognition and classification techniques.

Digital Image Processing, by R. Gonzalez and R. E. Wood, Prentice Hall of India.
Digital Image Processing using MATLAB by R. Gonzalez, R. E. Wood and Steven L. Eddins Gatesmark Publishing.
Introductory Computer Vision and Image Processing by Andrian Low. McGraw Hill.
Pattern Recognition-Statistical, Structural and neural approach by Robert Schalkoff, John Willey & Sons.
Fundamentals of Digital Image Processing by A K Jain, Prentice Hall.
Digital Image Processing by W.K. Pratt, McGraw Hill.