DIGITAL IMAGE PROCESSING

An Overview

Digital Image Processing An IntroductionAn image may be defined as : Two-dim. function, f(x, y), where x and y are spatial

(plane) coordinates. The amplitude of f at any pair of coordinates (x, y) is

called the intensity or gray level of the image at that point.

A 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 and Pixels

Digital Image Processing An Introduction

Digitization of a continuous image. The pixel at coordinates [m=10, n=3] has the integer brightness

value 110

An Example: A Monochrome Digital Image

The Pixel Values Of the Image

Changing Image Characteristics with Processing

Fundamental steps in Digital Image Processing

KNOWLEDGE BASE

ImageAcquisition

ImageEnhancement

Image Restoration

Color ImageProcessing

Wavelets &multiresolutionprocessing

Compression MorphologicalProcessing

Segmentation

Representation& Description

Object Recognition

Problem Domain

Image Acquisition

Involves Preprocessing Since the image is in the Digital form . It is the simplest step.

Image Enhancement

Enhancement for the specific applications. Image Enhancement approaches fall into

two broad categories:

1. Spatial Domain Methods

2. Frequency Domain Methods

Image Enhancement Spatial Domain Methods Spatial domain methods are procedures that

operate directly on the pixels Direct manipulation of the pixels in an image. Spatial Domain Processes will be

denoted by the expression: g(x, y)=T[f(x,y)] f(x,y)--- Input Image g(x,y)--- Processed Image T is an operator on f

Image Enhancement Frequency Domain Methods Discrete Fourier Transform (DFT) is the

foundation of most of the work of the image enhancement.

Enhancement in frequency domain is being applied for:

1.SMOOTHING FREQUENCY DOMAIN

2.SHARPENING FREQUENCY DOMAIN

Image Enhancement Filtering

Pre-Processing

Post-Processing

Fourier Transform

Filter Function H (u, v)

Inverse Fourier Transform

f (x, y))

F(u,v) G(u,v)

g (x,y)

INPUT IMAGE PROCESSED IMAGE

Image Enhancement Filtering(cont) Smoothing Frequency Domain Filters

Low pass filters

Sharpening Frequency Domain Filters

High pass filters

An Example

Image Restoration

Improvement in an image in some predefined sense.

Recover or reconstruct an image that has been degraded by using a priori knowledge of the degradation phenomenon.

Degradation Function

H

RestorationFunction

s+

Noiseη(x,y)

f(x,y) F(x,y)

g(x,y)

Color Image Processing

The use of color in image processing is motivated by two principal factors: Color is a powerful descriptor that often

simplifies object identification and extraction from the scene.

Humans can discern thousands of color shades and intensities, compared to about only two shades of gray. This factor is important in manual image analysis.

Color Image Processing (cont.)Color image processing is divide

into two major areas:

Full color processing Pseudo color processing

Wavelets and Multiresolution Processing The wavelet transform makes it even easier to compress, transmit

and analyze many images. Wavelet transforms are based on small

waves, called WAVELETS, of varying frequency and limited duration.

Image Compression

Removal of redundant data. Applications include tele-video conferencing,

FAX etc..

Compression techniques fall into two

categories:• Information Preserving• Lossy compression

Morphological Image Processing Extracting image components that are useful

in the representation and description of region shape ,such as boundaries etc..

Major morphological operations are: Dilation Erosion Open Close

Morphological Image Processing Dilation & Erosion

Image Erosion Dilation

Morphological Image Processing Open & Close

Image with structure element

Opening

Image Segmentation

Image segmentation algorithms generally are

based on one of the two basic properties of the

intensity values: Discontinuity

Line Detection, Edge Detection Similarity

Thresholding,region growing and merging

Representation and Description Representation of a region involves two

choices: External characteristics

Shape characteristics Internal characteristics

Color and Texture

Object or Pattern RecognitionTwo approaches are being developed for

pattern recognition:

Decision –theoretic Methods

1. Matching

2. Neural Networks Structural Methods

1. String Matching

Need Of Digital Image Processing Vision allows humans

to perceive and understand the world surrounding us .

A machine recognition system has real difficulties reading broken characters

APPLICATIONS

remote sensing via satellites and other spacecrafts

image transmission and storage for business applications

Medical Processing radar, sonar, and acoustic image processing robotics Automated inspection of industrial parts.etc.

Applications (contd.)

Thank You