EELE 5310: Digital Image Processing

Lecture 1

Introduction

Introduction

Digital image Processing (DIP) course isdesigned to introduce the concepts related todigital images, provide insight into basic digitalimage processing operations and introduce thebasic 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 ofdigital image processing including image acquisition,transformation, compression, enhancement, restoration,analysis, and so on.

Be able to use MATLAB to implement basic imageprocessing algorithms and get familiar with somefunctions provided by MATLAB image processingtoolbox.

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 EMspectrum

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