Image Processing

Technical Paper
On
Image Processing

From:
Govt. Polytechnic,
Khamagaon


Submitted By:
1.Vishal Bhagwat Tathe
vishaltathe6@gmail.com

2.Pawan Suresh Bathe
bathepawan@gmail.com

S ubmitted To:
Priyadarshini College Of Engineering
Nagpur





Self Declaration
We Declare That This Paper Is Prepared And Compiled By Us.

Abstract:
This paper describes different methods and technology that deals with the image processing where In real world an image is considered as the function of two real variables say x and y. Image processing is referred to processing of a 2D picture by a computer. In image processing an image is first converted into a digital form before processing an image, digitization includes sampling of image and quantization of sampled values.
After digitization i.e. after converting the image into bit information, processing is performed. Digital image processing is being used in many domains today. One of the example include medical science.Image processing touches the medical field in many ways, it plays vital role in various fields of medical science from Ophthalmology and radiology to orthodontics. The various image processing technique are image enhancement, image restoration and image compression.
Image enhancement refers to accentuation, or sharpening, of image features such as boundaries. It is the processing of images to improve their appearance to human viewers or to enhance the performance of other image processing system. Image restoration is concerned with filtering the observed image to minimize the effect of degradations. In most applications involving images one of the most common problems is the presence of noise so image processing can be useful. Image compression is concerned with minimizing the no of bits required to represent an image.
Tex compression - CCITT GROUP3 & GROUP4Still image compression - JPEGVideo image compression -MPEG




















CONTENTS

1 Image as a Function

2. What’s the Image Processing?

3. What Is Digital Image Processing?

4. Techniques of Image processing
i. Image enhancement
ii. Image restoration
iii. Image Compression

5. Some Image processing Software

a.Meade Autostar IP astronomical software
b.The IDL Intelligent Tools (iTools)
c.Adobe Photoshop

6. Reference

7. Conclusion










Image as a Function

In real world an image is considered as the function of two real variables say x and y. Image processing is referred to processing of a 2D picture by a computer. In image processing an image is first converted into a digital form before processing an image, digitization includes sampling of image and quantization of sampled values.
.

What’s the Image Processing?

Image processing allows you to modify the appearance of an image by applying various types of filters, scaling options or transformations. The simplest type of processing is linear scaling. With linear scaling, one pixel from the source image is multiplied by a scale factor, then an offset term is added. The original pixel value is then replaced with the resulting value. This process is repeated on each pixel in the image.
The contrast and brightness of the image can be controlled by varying the scale factor (contrast) and the offset value (brightness). You should attempt to set the contrast to a value that allows the full range of the important parts of the image to fall on the sloping section of the transfer curve function), then set the brightness so that the minimum pixel values appear black or very dark gray. A image processing techniques usually transform images into other images, (enhancement, correcting blurred, out-of-focus, compression ® better 2D projection image for CV).The task of information recovery is left to human user.



Image Processing (IP) is used for two somewhat different purposes:
A . improving the visual appearance of images to a human view, and
b. preparing images for measurement of the features and structures present.
Image Processing:= Image ® Image
Transformation


What Is Digital Image Processing?
An image may be defined as a two-dimensional function, fxy(,), where x and y are spatial coordinates, and the amplitude of f at any pair of coordinates (,)xy 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. 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 referred to as picture elements, image elements, pels, and pixels. Pixel is the term used most widely to denote the elements of a digi­tal image.
Techniques of Image processing:

1.Image enhancement

Image enhancement refers to accentuation, or sharpening, of image features such as boundaries. It is the processing of images to improve their appearance to human viewers or to enhance the performance of other image processing system.

Ø Image enhancement Techniques :


Histogram equalization

USE: To improve contrast of an image. Can be used on a whole image or just on . a part of an image.

THEORY: where G is total number of gray levels.

OPERATION:
if t(g) is the actual number of pixels at old gray level g or less, then




.


2. General gray-level transformations
USE: To combine thresholding, bunching and splitting into one operation. This is also useful as a single tool in a set of software tools for image processing, since it does anyone of the technique on any part of the histogram.
OPERATION: A function F(g) can be specified which transforms any gray level g to a new gray level q.


3. convolution: Another useful image processing technique, convolution, allows you to enhance the appearance of animage by 'filtering' the image data by either smoothing (low-pass filter) or sharpening (high-pass filter) or combining parts of both types of filters.
This is accomplished by passing a convolution matrix, or kernel, over the entire image and replacing the center pixel in the resulting matrix with the scaled sum of all of the other values. For example, if you wanted to average each pixel with its adjacent neighbors the following kernel could be used.
11 1
11 1
11 1
Each pixel, including the center one, would be multiplied by 1, then the sum of each of these terms is calculated. The result is then divided by the sum of each of the kernel values, in this case 9. Finally, the center pixel value is replaced with the new result. This process is then repeated for every pixel in the image. There are many different kernels that you can create that give various results.



2.Image restoration:

Image restoration is concerned with filtering the observed image to minimize the effect of degradations. In most applications involving images one of the most common problems is the presence of noise so image processing can be useful.
Technique : Recursive filtering
USE: To improve a sequence of still images--reducing noise by producing new images that correspond to weighted averages of previous images.

OPERATION: It is assumed that newly collected images are available from a frame store with a fixed delay between each image.
1. Setting up:
copy an image into a separate frame store, dividing all the gray levels by an integer, n. Add to that image n-1 subsequent images, the gray levels of which are also divided by n. You should now have the average of the first n images in the frame store.
2. Recursion:
For every new image, multiply the contents of the frame store by (n-1)/n and the new image by 1/n, add them together and put the result back into the frame store.


Technique : Background subtraction
USE: To remove light shading or discover movement from one image to another.

THEORY: Gray level in each pixel in one image is subtracted from the gray level in the corresponding pixel in other image, i.e.
result = x-y (x >= y)
If x < y the result is negative: -1 is held as 255
-2 is held as 254
and so on
A better operation: result = x - y

OPERATION: Collect both images. One image, x, should be on a pure background. The image with object will form image y. For each pixel calculate
abs(x-y)
This will give a negative image of the object. To return the image to a positive, use the following formula
new image = MAX - abs(x-y)

.Other techniques of Restoration :
· Wiener filter
· Richardson-Lucy method



3. Image Compression:
Compression of images: problem of storing them in a form that systems need to get the following benefits:
- speedily operation (both compression and unpacking),
- significant reduction in required memory, no significant loss of quality in the image,
- format of output suitable for transfer or storage.
Each of this depends on the user and the application.
A typical data compression system.
Compression Techniques:
Run Length Encoding
Huffman Coding
Modified Huffman Coding
Modified READ
Arithmetic Coding
LZW
JPEG
Other state-of-the-art image compression methods: Fractal and Wavelet compression.

Many file formats use compression to reduce the file size of bitmap images. Lossless techniques compress the file without removing image detail or color information; lossy techniques remove detail. The following are commonly used compression techniques:

RLE (Run Length Encoding)
Lossless compression; supported by some common Windows file formats.

LZW (Lemple-Zif-Welch)
Lossless compression; supported by TIFF, PDF, GIF, and PostScript language file formats. Most useful for images with large areas of single color.

JPEG (Joint Photographic Experts Group)
Lossy compression; supported by JPEG, TIFF, PDF, and PostScript language file formats. Recommended for continuous-tone images, such as photographs. To specify image quality, choose an option from the Quality menu, drag the Quality pop-up slider, or enter a value between 0 and 13 in the Quality text box. For the best printed results, choose maximum-quality compression. JPEG files can be printed only on Level 2 (or later) PostScript printers and may not separate into individual plates.

CCITT
A family of lossless compression techniques for black-and-white images; supported by the PDF and PostScript language file formats. (CCITT is an abbreviation for the French spelling of International Telegraph and Telekeyed Consultive Committee.)

ZIP
Lossless compression; supported by PDF and TIFF file formats. Like LZW, ZIP compression is most effective for images that contain large areas of single color.

PackBits (ImageReady)

Lossless compression that uses a run-length compression scheme; supported by the TIFF file format in ImageReady only.

Some Image processing Software

1.Meade Autostar IP astronomical image processing software
Introduction:
Meade Autostar IP is astronomical image processing software.You can perform many of the same image processing tasks that a professional astronomer would do on a large institutional
computer. With Meade Autostar IP you can:
• Enhance high resolution images using advanced image processing techniques.
• Determine stellar magnitudes directly from electronic images, and a number of other powerful
features.
A Note on Accuracy
Meade Autostar IP was designed to be extremely powerful and accurate. Many programs designed for the PC have taken shortcuts to appear faster, but at the expense of accuracy! Not Meade Autostar IP! By carefully coding each section of the program using 64 bit floating point numbers, Meade Autostar IP takes full advantage of your computer to provide fast and accurate performance. This gives you the power and accuracy that rivals main-frame computer performance.
System Requirements
The minimum system required to run Meade Autostar IP are as follows:
• 64 MB RAM.
• Hard Disk with 20 Megabytes of free space.
• An VGA or better adapter and compatible monitor.
• Microsoft Windows 98SE or above.
• A Microsoft compatible mouse.

2.The IDL Intelligent Tools (iTools)
The IDL Intelligent Tools (iTools) are a set of interactive utilities that combine dataanalysis and visualization with the ability to produce presentation quality graphics.
The iTools allow users to continue to benefit from the control of a programming
language, while enjoying the convenience of a point-and-click environment. There are 7 primary iTool utilities built into the IDL software package. Each of these seven
tools is designed around a specific data or visualization type :
• Two and three dimensional plots (line, scatter, polar, and histogram style)
• Surface representations
• Contours
• Image displays
• Mapping
• Two dimensional vector flow fields
• Volume visualizations
Digital Images and Advanced iImage Operations
IDL provides a powerful environment for image processing and display. Digital
images are easily represented as two-dimensional arrays in IDL and can be
processed just like any other array. Within an image array the value of each pixel
represents the intensity and/or color of that position in the scene. Images of this
form are known as sampled or raster images, because they consist of a discrete grid
of samples. IDL contains many procedures and functions specifically designed for
image display and processing. In addition, the iImage tool allows the user great
flexibility in manipulating and visualizing image data.

1. Adobe Photoshop


The Photoshop and ImageReady work area is arranged to help you focus on creating and editing images.
Adobe® Photoshop® 7.0, the professional image-editing standard. With its integrated Web tool application, Adobe ImageReadyR, Photoshop delivers a comprehensive environment for professional designers and graphics producers to create sophisticated images for print, the Web, wireless devices, and other media. Moreover, Photoshop provides a consistent work environment with other Adobe applications including Adobe Illustrator®, Adobe InDesign®, Adobe GoLive®, Adobe LiveMotion™, Adobe After Effects®, and Adobe Premiere®.


Installing Adobe Photoshop and ImageReady
You must install Photoshop and ImageReady from the Adobe Photoshop CD onto your hard drive; you cannot run the program from the CD.
Follow the on-screen installation instructions. For more detailed information, see the HowToInstall file on the CD. The single installer installs both Photoshop and ImageReady.
Reference:


Websites
1.Http://www.uspto.gov/patft/help/contents.htm
2.http://newslab.cs.wayne.edu/jise.pdf
3.http://faculty.cs.tamu.edu/dzsong/teaching/fall2004/netbot/John_G.ppt
4.www.cs.sunysb.edu/~hgupta/ps/coverage.ps
5.http://www.ittvis.com/
6.http://www.cee.hw.ac.uk/hipr/html/morops.html
7.http://www.mathworks.com/
Books
• R. G. van Schyndel, A. Z. Tirkel, and C. F. Osborne, “A Digital Watermark”, Proc. of the IEEE Int. Conf. on Image Processing, vol. 2, pp. 86–90, Austin, Texas, Nov 1994.
• [Gonzalez92] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Addison-Wesley, 1992, ISBN 0-201-50803-6.
• R. C. Gonzalez and R. E. Woods Digital Image Processing, Addison-Wesley, New York, 199, Chap 9.
• E. Davies Machine Vision: Theory, Algorithms and Practicalities, Academic Press, 1990, pp 149 - 161.
• R. Haralick and L. Shapiro Computer and Robot Vision, Vol. 1, Addison-Wesley Publishing Company, 1992, Chap. 5.
• Jain Fundamentals of Digital Image Processing, Prentice-Hall, 1989, Chap. 9.



Conclusion:
In this way we have presented the paper entitled “Image Processing” and came to know that, image processing is being used in many domains today and plays vital role in many computer engineering and science fields. One of the example include medical science.Image processing touches the medical field in many ways, it plays vital role in various fields of medical science from Ophthalmology and radiology to orthodontics..
Image processing allows us to modify the appearance of an image by applying various types of filters, scaling options or transformations.
The various image processing technique are image enhancement, image restoration and image compression etc.
The are various image processing softaware are available in market like adobe photoshop, Meade Autostar IP, iTools etc.