1. DIGITAL IMAGE PROCESSING
An Overview

2. Digital Image Processing An Introduction
An 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

3. Digital Image Processing An Introduction
Digitization of a continuous image. The pixel at
coordinates [m=10, n=3] has the integer brightness
value 110

4. An Example: A Monochrome Digital Image

5. The Pixel Values Of the Image

6. Changing Image Characteristics with Processing

7. Fundamental steps in Digital Image Processing
KNOWLEDGE BASE
Image
Acquisition
Enhancement
Restoration
Color Image
Processing
Wavelets &
multiresolution
processing
Compression
Morphological
Segmentation
Representation
& Description
Object
Recognition
Problem Domain

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

9. Image Enhancement Enhancement for the specific applications.
Image Enhancement approaches fall into two broad categories:
1. Spatial Domain Methods
2. Frequency Domain Methods

10. 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

11. 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

12. 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

13. Image Enhancement Filtering(cont)
Smoothing Frequency Domain Filters
Low pass filters
Sharpening Frequency Domain Filters
High pass filters

14. An Example

15. 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.
g(x,y)
Degradation
Function H
Restoration s +
f(x,y)
F(x,y)
Noiseη(x,y)

16. 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.

17. Color Image Processing (cont.)
Color image processing is divide
into two major areas:
Full color processing
Pseudo color processing

18. 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.

19. Image Compression Removal of redundant data.
Applications include tele-video conferencing, FAX etc..
Compression techniques fall into two
categories:
Information Preserving
Lossy compression

20. 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

21. Morphological Image Processing Dilation & Erosion

22. Morphological Image Processing Open & Close
Image with structure element
Opening

23. 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

24. Representation and Description
Representation of a region involves two
choices:
External characteristics
Shape characteristics
Internal characteristics
Color and Texture

25. Object or Pattern Recognition
Two approaches are being developed for
pattern recognition:
Decision –theoretic Methods
1. Matching
2. Neural Networks
Structural Methods
1. String Matching

26. 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

27. 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.

28. Applications (contd.)

29. Thank You