Digital Image Processing

CONTENTS

What is an image?

Digital image processing-Introduction

History

Types of computerised process

Fundamental steps in image processing

Sources for images

Uses

Applications

What is an image?

An image may be defined as a two-
dimensional function, f(x, y), where x and y are
spatial (plane) coordinates, and the amplitude
of at any pair of coordinates (x, y) is called the
Intensity or gray level of the image at that
point.

When x, y, and the amplitude values of f are
all finite,discrete quantities, we call the image a
digital image.

Digital Image Processing

The field of digital image processing refers to
processing digital images by means of a digital
computer.
Note :-

Digital image is composed of a finite number of
elements, each of which has a particular
location and value.These elements are
referred to as picture elements, image
elements, pels and pixels.

Pixel is the term most widely used to denote
the elements of a digital image.

History

Many of the techniques of digital image
processing, or digital picture processing as it
often was called, were developed in the 1960s
at the Jet Propulsion Laboratory,
Massachusetts Institute of Technology, Bell
Laboratories, University of Maryland, and a few
other research facilities.

History

With the fast computers and signal processors
available in the 2000s, digital image
processing has become the most common
form of image processing and generally, is
used because it is not only the most versatile
method, but also the cheapest.

Digital image processing technology for
medical applications was inducted into the
Space Foundation Space Technology Hall of
Fame in 1994.

History

In 2002 Raanan Fattal introduced Gradient
domain image processing, a new way to
process images in which the differences
between pixels are manipulated rather than the
pixel values themselves.

Age progression of
missing person
Aundria Bowman

Types of computerized
process

There are no clear-cut boundaries in the
continuum from image processing at one end
to computer vision at the other. However, one
useful paradigm is to consider three types of
computerized processes in this continuum:
low-, mid-, and high-level processes.

process

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
factthat both its inputs and outputs are
images.

process

Mid-level processing on images involves
tasks such as segmentation (partitioning an
image into regions or objects), description of
those objects to reduce them to a form suitable
for computer processing, and classification
(recognition) of individual objects.

A mid-level process is characterized by the fact
that its inputs generally are images, but its
outputs are attributes extracted from those
images (e.g., edges, contours, and the identity
of individual objects).

process

Higher-level processing involves “making
sense” of anensemble of recognized
objects, as in image analysis, and, at
the far end of the continuum,performing the
cognitive functions normally associated with
vision and, in addition,encompasses
processes that extract attributes from images,
up to and including the recognition
ofindividual objects.

process

As a simple illustration to clarify these
concepts, consider the area of automated
analysis of text.

The processes of acquiring an image of the
area containing the text,preprocessing that
image, extracting (segmenting) the individual
characters, describing the characters in a
form suitable for computer processing and
recognizing those individual characters are in
the scope of what we call digital image
processing.

Fundamental steps in image
processing

Image Acquisition

This is the first step or process of the
fundamental steps of digital image processing.

Image acquisition could be as simple as being
given an image that is already in digital form.
Generally, the image acquisition stage involves
preprocessing, such as scaling etc.

Topics:-
• Basic digital image concepts
• Preprocessing stages
• Visual perception
• Sampling
• Quantization
• Pixel operations

Image Enhancement

Image enhancement is among the simplest and
most appealing areas of digital image
processing. Basically, the idea behind
enhancement techniques is to bring out detail
that is obscured, or simply to highlight certain
features of interest in an image. Such as,
changing brightness & contrast etc.


Enhancement in the spatial domain

Point processing
 Log transformation
 Power law transformation

Spatial filtering process

Smoothing filters

Frequency Domain Filtering
The Fourier transform
Filtering in the frequency domain
Low pass filters - High pass
filters
->Ideal low pass filter
->Butterworth low pass
filter
->Gaussian low pass filter

One of the most common
uses of DIP techniques:

improve quality,

remove noise.. etc

Image Restoration

Image restoration is an area that also deals
with improving the appearance of an image.
However, unlike enhancement, which is
subjective, image restoration is objective, in the
sense that restoration techniques tend to be
based on mathematical or probabilistic models
of image degradation.

Color Image Processing

Color image processing is an area that has
been gaining its importance because of the
significant increase in the use of digital images
over the Internet. This may include color
modeling and processing in a digital domain
etc.

Topics:
• Color fundamentals
• Color models
• Color transformations
• Smoothing and sharpening
• Color segmentation
• Noise in color images

Wavelets &Multiresolution
Processing

Wavelets are the foundation for representing
images in various degrees of resolution.
Images subdivision successively into smaller
regions for data compression and for pyramidal
representation.

Compression

Compression deals with techniques for
reducing the storage required to save an image
or the bandwidth to transmit it. Particularly in
the uses of internet it is very much necessary to
compress data.

Topics:
• Coding redundancy
• Image compression models
• Error-free compression
• Lossy compression
• Image compression standards

Morphological Processing

Morphological processing deals with tools for
extracting image components that are useful in
the representation and description of shape.

Basic morphological concepts and operations
 Hitting, fitting and missing
 Erosion and dilation
 Opening and closing

Morphological algorithms
 Boundary extraction
 Region filling

Segmentation

Segmentation procedures partition an image
into its constituent parts or objects. In general,
autonomous segmentation is one of the most
difficult tasks in digital image processing.

A rugged segmentation procedure brings the
process a long way toward successful solution
of imaging problems that require objects to be
identified individually.

Main topics:

The segmentation problem

Importance of good thresholding

Problems that can arise with
thresholding

The basic global thresholding
algorithm

Point- edge detection

Region-based segmentation

Representation & Description

Representation and description almost always
follow the output of a segmentation stage,
which usually is raw pixel data, constituting
either the boundary of a region or all the points
in the region itself.

Choosing a representation is only part of the
solution for transforming raw data into a form
suitable for subsequent computer processing.

Description deals with extracting attributes
that result in some quantitative information of
interest or are basic for differentiating one class
of objects from another.

Topics:
• Chain codes
• Skeletons
• Boundary descriptors
• Regional descriptors
• Texture

Object recognition

Recognition is the process that assigns a label,
such as, “vehicle” to an object based on its
descriptors.
Topics:
• Pattern classes
• Structural methods

Knowledge Base

Knowledge may be as simple as detailing
regions of an image where the information of
interest is known to be located, thus limiting the
search that has to be conducted in seeking that
information.

The knowledge base also can be quite
complex, such as an interrelated list of all major
possible defects in a materials inspection
problem or an image database containing high-
resolution satellite images of a region in
connection with change-detection applications.

Sources for Images
One of the simplest ways to develop a basic
understanding of the extent of image
processing applications is categorization
according to sources.

Electromagnetic (EM) energy spectrum

Synthetic images produced by computer

Acoustic

Ultrasonic

Electronic

Electromagnetic (EM)
energy spectrum

Images based on radiation from the EM
spectrum are the most familiar to us.

If bands are grouped according to energy
per photon, we obtain the spectrum

Em spectrum are not distinct but rather
transition smoothly one to other.

Uses

Gamma-ray imaging (highest energy):
nuclear medicine and astronomical
observations

X-rays: medical diagnostics, industry, and
astronomy, etc.

Ultraviolet: industrial inspection,
microscopy, lasers, biological imaging, and
astronomical observations

Visible and infrared bands: light
microscopy, astronomy, remote sensing,
industry, and law enforcement

Microwave band: radar Radio band
(lowest energy) : medicine (such as MRI) and
astronomy

APPLICATIONS
The Hubble Telescope

Launched in 1990 the Hubble
telescope can take images of very
distant objects.
However, an incorrect mirror made
many of Hubble’s images useless.

Image processing techniques were
used to fix this

HCI
Try to make human computer interfaces more
natural.
 Face recognition
 Gesture recognition

Artistic Effects

Artistic effects are used to make
images more visually appealing, to add
special effects and to make composite
images.

Image Processing
Examples

Pseudocolor enhancement for security
screening.

Earthquake Analysis

UV Imaging

Extraction of settlement area from an aerial
image

Face Morphing

Fingerprint Recognition

Iris Recognition

Hand Writing Recogition

Face Detection

Digital Image Processing

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