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CSE-IT-312 DIP -2 Definition Steps and Application

Digital Image Processing (DIP) involves the manipulation of digital images for various applications, including enhancement for human interpretation and efficient data processing. It encompasses a range of processes from low-level operations like noise reduction to high-level tasks such as object recognition and image analysis. The document outlines fundamental steps in DIP, various applications, and the importance of knowledge bases in guiding image processing tasks.

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CSE-IT-312 DIP -2 Definition Steps and Application

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montysid22

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Digital Image Processing

By
Dr. Bhupendra Singh Kirar
Assistant Professor
Department of Electronics and Communication Engineering
Indian Institute of Information Technology, Bhopal
Necessity of Digital Image Processing?

 Interest in digital image processing methods stems from two

principal application areas:

 Improvement of pictorial information for human interpretation, and

 Processing of image data for tasks such as storage, transmission,

and extraction of pictorial information.
Digital Image Processing

 Subject Definition

 Digital Image Processing

 Digital

 Image

 Processing

 In means processing the image data in digital domain.

 Processing may be in application specific manner.

What is Digital Image Processing?

 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 f
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 intensity values of f are all finite, discrete
quantities, we call the image a digital image.

 The field of digital image processing refers to processing digital

images by means of a digital computer.
 Note that a digital image is composed of a finite number of
elements, each of which has a particular location and value. These
elements are called picture elements, image elements, pels, and
pixels.
 Pixel is the term used most widely to denote the elements of a digital
image.
What is Digital Image Processing?
What is Digital Image Processing?
What is Digital Image Processing?
What is Digital Image Processing?

 Vision is the most advanced of our senses, so it is not surprising that

images play the single most important role in human perception.
However, unlike humans, who are limited to the visual band of the
electromagnetic (EM) spectrum, imaging machines cover almost the
entire EM spectrum, ranging from gamma to radio waves.

 They can operate on images generated by sources that humans are

not accustomed to associating with images.
 These include ultrasound, electron microscopy, and computer
generated images.

 Thus, digital image processing encompasses a wide and varied field

of applications.

 The area of image analysis (also called image understanding) is in between image
processing and computer vision.
What is Digital Image Processing?

The continuum from image processing to computer vision can be

broken up into low-, mid- and high-level processes
What is Digital Image Processing?

The continuum from image processing to computer vision a useful

paradigm is to consider three types of computerized processes are:
low-, mid- and high-level 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 processing of 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).
What is Digital Image Processing?

 Higher-level processing involves “making sense” of an ensemble of

recognized objects, as in image analysis, and, at the far end of the
continuum, performing the cognitive functions normally associated
with human vision.
The Origins of Digital Image Processing
The Origins of Digital Image Processing
The Origins of Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Examples of Fields that Use Digital Image Processing
Fundamental Steps in Digital Image Processing
Fundamental Steps in Digital Image Processing

Image acquisition is the first process. 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.

Image enhancement is the process of manipulating an image so the

result is more suitable than the original for a specific application. The
word specific is important here, because it establishes at the outset that
enhancement techniques are problem oriented. Thus, for example, a
method that is quite useful for enhancing X-ray images may not be the
best approach for enhancing satellite images taken in the infrared band
of the electromagnetic spectrum.
Fundamental Steps in Digital Image Processing

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. Enhancement, on the other hand, is based on
human subjective preferences regarding what constitutes a “good”
enhancement result.

Color image processing is an area that has been gaining in

importance because of the significant increase in the use of digital
images over the internet. Color is used also as the basis for extracting
features of interest in an image.
Fundamental Steps in Digital Image Processing

Wavelets are the foundation for representing images in various

degrees of resolution. In particular, this material is used in the book for
image data compression and for pyramidal representation, in which
images are subdivided successively into smaller regions. In addition to
wavelets, a number of other transforms that are used routinely in image
processing will be discussed.

Compression, as the name implies, deals with techniques for reducing

the storage required to save an image, or the bandwidth required to
transmit it. Although storage technology has improved significantly over
the past decade, the same cannot be said for transmission capacity.
This is true particularly in uses of the internet, which are characterized
by significant pictorial content. Image compression is familiar (perhaps
inadvertently) to most users of computers in the form of image file
extensions, such as the jpg file extension used in the JPEG (Joint
Photographic Experts Group) image compression standard.
Fundamental Steps in Digital Image Processing

Morphological processing deals with tools for extracting image

components that are useful in the representation and description of
shape.

Segmentation partitions 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. On the other hand,
weak or erratic segmentation algorithms almost always guarantee
eventual failure. In general, the more accurate the segmentation, the
more likely automated object classification is to succeed.
Fundamental Steps in Digital Image Processing

Feature extraction almost always follows the output of a segmentation

stage, which usually is raw pixel data, constituting either the boundary
of a region (i.e., the set of pixels separating one image region from
another) or all the points in the region itself. Feature extraction consists
of feature detection and feature description.

Feature detection refers to finding the features in an image, region, or

boundary. Feature description assigns quantitative attributes to the
detected features. For example, we might detect corners in a region,
and describe those corners by their orientation and location; both of
these descriptors are quantitative attributes.
Fundamental Steps in Digital Image Processing

Image pattern classification is the process that assigns a label (e.g.,

“vehicle”) to an object based on its feature descriptors. we will discuss
methods of image pattern classification ranging from “classical”
approaches such as minimum-distance, correlation, and Bayes
classifiers, to more modern approaches implemented using deep
neural networks. In particular, we will discuss in detail deep
convolutional neural networks, which are ideally suited for image
processing work.
modules.
Fundamental Steps in Digital Image Processing

So far, we have said nothing about the need for prior knowledge or
about the interaction between the knowledge base and the processing
modules in Fig. 1.23. Knowledge about a problem domain is coded into
an image processing system in the form of a knowledge database. This
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 can also 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. In addition to guiding
the operation of each processing module, the knowledge base also
controls the interaction between modules.
Fundamental Steps in Digital Image Processing

Morphologic
Image
al
Restoration
Processing

Image
Segmentatio
Enhancemen
n
t