EELE 5310: Digital Image Processing
Lecture 1
Introduction
Introduction

Digital image Processing (DIP) course is
designed to introduce the concepts related to
digital images, provide insight into basic digital
image processing operations and introduce the
basic algorithms used for such purposes.

Textbook: Digital Image Processing, Rafael C.
Gonzalez and Richard E. Woods, Second Edition,
Prentice-Hall, 2001.
Prerequisites:
Knowledge of the following three areas:

Linear Algebra.

Signals and Systems.

Programming skills
Grading Policy

Quizzes & H.W 20%

Midterm Exam 25%

Project 15%

Final Exam 40%
Outcomes

By the end of this semester ,you will Know basics of
digital image processing including image acquisition,
transformation, compression, enhancement, restoration,
analysis, and so on.

Be able to use MATLAB to implement basic image
processing algorithms and get familiar with some
functions provided by MATLAB image processing
toolbox.
Course outline
Introduction
 Digital Image Fundamentals
 Image Enhancement in the Spatial Domain
 Image Enhancement in the Frequency Domain
 Image Restoration
 Image Compression
 Image Segmentation
 Image Representation and Description
 Color image processing

What is a Digital Image?

A finite array MxNof data values
What is Image Processing

Processing digital images by means of a digital computer.

Image processing typically attempts to accomplish one of three things:

Restoring Images

Enhancing Images

Understanding Images
• Restoration takes a corrupted image and attempts to recreate a clean original.
• Enhancement alters an image to makes its meaning clearer to human observers.
• Understanding usually attempts to mimic the human visual system in extracting
meanings from an image
Three Types of Processes

Low-level Processes :

Involve primitive operations such as image preprocessing to reduce noise, contrast
enhancement, and image sharpening.


A low-level process is characterized by the fact that both its inputs and outputs are images.
Mid-level Processes:

Involves tasks such as segmentation (partitioning an image into regions or objects), description
of those objects to reduce them to a form suitable for machine learning , and
classification(recognition) of individual objects.

Its inputs generally are images, but its outputs are attributes extracted from those images (e.g.,
edges, contours, and the identity of individual objects).
Three Types of Processes

cont…
High-level Processes :

Processing involves "making sense“ of an ensemble of recognized objects, as in image
analysis, and object recognition.
Sources of Energy for Image Formation

The principle energy source for images is the EM
spectrum
Some Applications -- Medical Diagnostics
Some Applications -- MRI
Imaging in Radio Band
Some Applications -- Industrial Inspection
Some Applications -- Remote Sensing
Some Applications -- CBIR
Some Applications -- Transmitting Images