This document provides an overview of image processing. It discusses acquiring images through various methods like cameras and converting them to digital formats. It also covers preprocessing techniques like enhancement, restoration and geometry transformations. Additional topics include image compression, analysis through techniques like segmentation and pattern recognition, and applications in medical imaging, remote sensing, and more. The document concludes by mentioning some common image processing software tools.

1. IMAGE PROCESSING

2. Presented & performed by
Arunachalam. PL
Nagaraj.K.N
COMPUTER ENGINEERING

3. • INTRODUCTION
• ACQUIRING IMAGES
– HUMAN RELIANCE ON IMAGES FOR
INFORMATION
– ELECTRONICS AND BANDWIDTH
LIMITATIONS
– HIGH RESOLUTION IMAGING
– COLOR IMAGING
– COLOR SPACES
– COLOR DISPLAYS
– IMAGE TYPES
• ITS TIME FOR DEMO
• CONCLUSION
• BIBLIOGRAPHY
IMAGE PROCESSING

4. Image processing involves processing or
altering an existing image in a desired
manner.
The next step is obtaining an image in a
readable format.
The Internet and other sources provide
countless images in standard formats.
IMAGE PROCESSING

5. Image processing are of two aspects..
improving the visual appearance
images to a human viewer
of
preparing images for measurement of
the features and structures present.
IMAGE PROCESSING

6. Since the digital image is “invisible” it must be
prepared for viewing on one or more output device
(laser printer,monitor,etc)
The digital image can be optimized for the application
by enhancing or altering the appearance of structures
within it (based on: body part, diagnostic task,
viewing preferences,etc)
It might be possible to analyze the image in the
computer and provide cues to the radiologists to help
detect
important/suspicious
structures
(e.g.:
Computed Aided Diagnosis, CAD)
IMAGE PROCESSING

7. Scientific instruments commonly produce
images to communicate results to the
operator, rather than generating an audible
tone or emitting a smell.
Space missions to other planets and Comet
Halley always include cameras as major
components, and we judge the success of those
missions by the quality of the images returned.
IMAGE PROCESSING

8. Image-to-image transformations
Image-to-information transformations
Information-to-image transformations
IMAGE PROCESSING

9. Enhancement (make image more useful, pleasing)
Restoration
Egg. deblurring ,grid line removal
Geometry
(scaling, sizing , Zooming, Morphing one object
to another).
IMAGE PROCESSING

10. Image statistics (histograms)
Histogram is the fundamental tool for analysis and
image processing
Image compression
Image analysis (image segmentation, feature
extraction, pattern recognition)
computer-aided detection and diagnosis (CAD)
IMAGE PROCESSING

11. Decompression of compressed image data.
Reconstruction of image slices from CT or MRI
raw data.
Computer graphics, animations and virtual reality
(synthetic objects).
IMAGE PROCESSING

12. The process of obtaining an high resolution (HR)
image or a sequence of HR images from a set of
low resolution (LR) observations.
HR techniques are being applied to a variety of
fields, such as obtaining
improved still images
high definition television,
high performance color liquid crystal display (LCD)
screens,
video surveillance,
remote sensing, and
medical imaging.
IMAGE PROCESSING

13. Conversion from RGB (the brightness of the individual red,
green, and blue signals at defined wavelengths) to YIQ/YUV
and to the other color encoding schemes is straightforward
and loses no information.
Y, the “luminance” signal, is just the brightness of a
panchromatic monochrome image that would be displayed
by a black-and-white television receiver
IMAGE PROCESSING

14. • Most computers use color monitors that have
much higher resolution than a television set but
operate on essentially the same principle.
• Smaller phosphor dots, a higher frequency
scan, and a single progressive scan (rather than
interlace) produce much greater sharpness and
color purity.
IMAGE PROCESSING

15. Digital processing requires images to be obtained in the
form of electrical signals. These signals can be digitized into
sequences of numbers which then can be processed by a
computer. There are many ways to convert images into
digital numbers. Here, we will focus on video technology, as
it is the most common and affordable approach.
IMAGE PROCESSING

16. • Multiple images may constitute a series of views of
the same area, using different wavelengths of light
or other signals.
• Examples include the images produced by
satellites, such as
– the various visible and infrared wavelengths recorded
by the Landsat Thematic Mapper(TM), and
– images from the Scanning Electron Microscope
(SEM) in which as many as a dozen different
elements may be represented by their X-ray
intensities.
• These images may each require processing.
IMAGE PROCESSING

17. A general-purpose computer to be useful for image
processing, four key demands must be met: highresolution image display, sufficient memory transfer
bandwidth, sufficient storage space, and sufficient
computing power.
A 32-bit computer can address
up to 4GB of memory(RAM).
IMAGE PROCESSING

18. • Adobe Photoshop
• Corel Draw
• Serif Photoplus
IMAGE PROCESSING

19. IMAGE PROCESSING

20. In electrical engineering and computer science, image
processing is any form of signal processing for which the
input is an image, such as photographs or frames of video;
the output of image processing can be either an image or a
set of characteristics or parameters related to the image.
Most image-processing techniques involve treating the
image as a two-dimensional signal and applying standard
signal-processing techniques to it.
IMAGE PROCESSING

21. Apply
Apply
Knowledge
Knowledge
Assimilate
Assimilate
Knowledge
Knowledge
Create
Knowledge
Seminar Knowledge
Sharing
And
Acquiring
Environment
Disseminate
Disseminate
Knowledge
Knowledge
IMAGE PROCESSING
Structure
Structure
Knowledge
Knowledge

22. This Paper has been submitted under the guidance of
K. Megala B.E
– Lecturer (Computer Engg).
M.Saravanan (M.E) – Lecturer (Computer Engg).
Over headed by
Mr.M. Ramesh Kumar, MCA.,Mphil
(Computer Engg).
IMAGE PROCESSING

23. “Things that think…
don’t make sense unless they link.”
Thank You
IMAGE PROCESSING

24. BIBLIOGRAPHY
John C. Ross. Image Processing Hand book, CRC Press. 1994.
[2] Peter Mc Curry, Fearghal Morgan, Liam Kilmartin. Xilinx FPGA
implementation of a pixel processor for object detection applications. In the
Proc. Irish Signals and Systems Conference, Volume 3, Page(s):346 – 349,
Oct. 2001.
[3] M. Moore. A DSP-based real time image processing system. In the
Proceedings of the 6th International conference on signal processing
applications and technology, Boston MA, August 1995.
Simplicity is the key to Victory.
Bruce Lee
IMAGE PROCESSING

25. IMAGE PROCESSING