SHREE DIGAMBER INSTITUTE OF TECHNOLOGY NEAR BHANDAREJ MOD NH-11,DAUSA

A SEMINAR PRESENTATION ON DIGITAL IMAGE PROCESSING

PRESENTED BY:- APOORVA VYAS/ECE/7TH SEM/12EDIEC001 KARAN KUMAR JOSHI/ECE/7TH SEM/12EDIEC007

CONTENTSIntroductionHistory of Image ProcessingFunctional CategoriesSteps in Image ProcessingNecessityFiltering in Image ProcessingTechnologiesAdvantages & DisadvantagesApplicationFuture Scope of Image Processing

INTRODUCTIONDIGITAL IMAGE PROCESSING generally refers to processing of a 2-D picture by a Digital Computer. In other sense it is the processing of any 2-D data. A Digital image is an array of real and complex no. represented by finite no of bits.

DIP has its several uses, processes and applications. Here we will know about them.

HIST0RY

Initially, the digital image processing was implemented newspaper industry. At that time , the first picture was sent by submarine cable between London and New York. In early 1920 the printing equipment codec picture was transmitted and was reconstructed at the receiving

The Bartlane picture transmission system was used to transport a picture across the Atlantic.

In 1960s, the computer perform the meaningful and powerful image processing tasks. It was known as the birth of digital image processing.

The first image of moon was taken by Ranger 7 on 31 july 1964 and was used for geometric corrections.

Alongwith the space applications , digital image processing is also used in medical imaging , remote earth resources, observations and astronomy in 1970s.

Functional Categories Image Enhancement

Information Extraction

Image Restoration

Functional categories in image processing

IMAGE RESTORATION f(x, y) g(x, y) f(x, y)

Noise n(x, y)

Degradation function

Restoration filter+

Image restoration technique is used to improve an image in some sense. This technique recover an image which has been degraded. So the restorations are oriented towards modelling the degradation and then applying the inverse process to recover original image.

The restoration is obtained an estimated image of the original image. The above shown model consist of a degradation function with an additive noise and a restoration filter .

Here the image f(x, y) degraded by degradation function and the noise is added so we got the new function g(x, y). Now this is fed to the restoration filter and we got the estimated image.

IMAGE ENHANCEMENT AND INFORMATION EXTRACTION

Image Enhancement : Enhancement is the modification of an image to alter its impact on the viewer

Information Extraction : utilize computers to provide corrected and improved images for study by human interpreters.

STEPS IN IMAGE PROCESSING

STEPS

IMAGE ACQUISITI

ON

WAVELETS

COMPRES-SION

SEGMENTAT-ION

COLOR IMAGE

PROCESSING

IMAG

E RE

STOR

A-TI

ON

IMAGE ENHANCE-MENT

IMAGE ACQUISITION

In this step, the image is captured by a sensor (such as a monochrome or colorTV camera) and digitized, if the output of the camera or sensor is not alreadyin digital form- an analog-to-digital converter (ADC) digitizes it.Camera:Camera consists of 2 parts:A lens that collects the appropriate type of radiation emitted from theobject of interest and that forms an image of the real object.Semiconductor device – so called charged coupled device or CCDwhich converts the irradiance at the image plan into an electrical signal.Frame GrabberFrame Grabber only needs circuits to digitize the electrical signal (standardvideo signal) from imaging sensor to store the image in the memory (RAM)of the computer.

IMAGE ENHANCEMENT

Image Enhancement is the process of manipulating an image so that the result is more suitable than the original for specific applications..

IMAGE RESTORATION

Improving the appearance of the image. Tend to be mathematical or probabilities models of image degradation.

COLOR IMAGE PROCESSING The human visual

system can distinguish hundreds of thousands of different colour shades and intensities.

In an image, a great deal of extra information may be contained in the colour, and this extra information can then be used to simplify image analysis, e.g. object identification and extraction based on colour.

Figure

Figure 1: The visible spectrum.

WAVELETS The wavelet transform plays an extremely

crucial role in image compression. For image compression applications, wavelet

transform is a more suitable technique compared to the Fourier transform.

Because the resulting function after Fourier transform is a function independent of time. On the other hand, wavelet transforms are based on wavelets which are varying frequency in limited duration. Due to the practicality of the wavelet transforms, this research paper is written to investigate the properties and the improvements that can be made to enhance the performance of the wavelet transforms.

COMPRESSION Image compression is minimizing the size in bytes of a graphics file without degrading the quality of the image to an unacceptable level.

The reduction in file size allows more images to be stored in a given amount of disk or memory space.

It also reduces the time required for images to be sent over the Internet or downloaded from Web pages.

SEGMENTATION Computer tries to separate objects

from the image background. It is one of the most difficult tasks in

DIP. Segmentation kinds: Autonomous Segmentation. Rugged Segmentation (long process to get successful solution). Erratic Segmentation.

NECESSITY OF IMAGE PROCESSING

The digital image is “invisible” .it must be prepared for viewing on one or more o/p devices(laser printer, monitor etc. )

The digital image can be optimized for the application by enhancing for altering the appearances of structures within it.

FILTERING IN IMAGE PROCESSING The filtering used to eliminating noise. This filter performs spatial filtering on each individual pixel in an image using the grey level values in a square or rectangular window surrounding each pixel.For example:a1 a2 a3 a4 a5 a6 3x3 filter window a7 a8 a9 

The average filter computes the sum of all pixels in the filter window and then divides the sum by the number of pixels in the filter window:Filtered pixel = (a1 + a2 + a3 + a4 ... + a9) / 9

TECHNOLOGIES

USED

TECHs

PIXELIZATION

COMPONENT

ANALYSIS

INDEPENDENT

ANALYSIS

HIDDEN MARKOV MODELS

SELF ORGANIZING MAPS

NEURAL NETWORK

S

WAVELETS

PIXELIZATIONThe result of enlarging a digital image further than the resolution of the monitor device, usually 72dpi (dots per inch), causing the individual pixels making up the image to become more prominent, thus causing a grainy appearance in the image.

Blurring a part of a picture by grouping pixel areas.

PRINCIPLE COMPONENT ANALYSIS Principal component analysis PCA

belongs to linear transforms based on the statistical techniques.

This method provides a powerful tool for data analysis and pattern recognition which is often used in signal and image processing.

As a technique for data compression, data dimension reduction.

There are various algorithms based on multivariate analysis or neural networks that can perform PCA on a given data set.

It introduces PCA as a possible tool in image enhancement and analysis.

INDEPENDENT COMPONENT ANALYSIS

Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals.

The main concept of ICA applied to images insists on the idea that each image (subimage) may be perceived as linear superposition of features ai(x, y) weighted by coefficients si.

In case of ICA, features are represented by columns of mixing matrix ai and si are elements of appropriate sources.

HIDDEN MARKOV MODEL A hidden Markov model (HMM) is a statistical

Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states.

An HMM configuration is described for many-dimensional image processing by several different ways (line-by-line, series of presenting elements, etc.).

The applications to the model calculations and binary image recovery.

SELF ORGANIZING MAPS In image processing the Self Organizing

Maps are used for Image classification and retrieval(CBIR) ,group the images in different classes.

The SOM (Self Organizing Map) Neural Network or commonly called a Kohonen Neural Network system is one of the unsupervised learning model that will classify the units by the similarity of a particular pattern to the area in the same class.

WAVELETSThe wavelet transform plays an extremely crucial role in image compression. For image compression applications, wavelet transform is a more suitable techniquecompared to the Fourier transform.

The resulting function after Fourier transform is a function independent of time.

On the other hand, wavelet transforms are based on wavelets which are varying frequency in limited duration.

Due to the practicality of the wavelet transforms, this research paper is written to investigate the properties and the improvements that can be made to enhance the performance of the wavelet transforms.

ADVANTAGES AND DISADVANTAGES

ADVANTAGES:- Digital image processing made digital image can be

noise free . It can be made available in any desired format. (X-rays,

photo negatives, improved image, etc) Digital imaging is the ability of the operator to post-

process the image .It means manipulate the pixel shades to correct image density and contrast .

Images can be stored in the computer memory and easily retrieved on the same computer screen .

Digital imaging allows the electronic transmission of images to third-party providers

DISADVANTAGES:- The initial cost can be high depending on

the system used . If computer is crashes then pics that

have not been printed and filed into Book Albums that are lost.

Digital cameras which are used for digital image processing have some disadvantages like:

Memory Card Problems Higher Cost Battery Consumption

APPLICATIONS

Medical

Digital Cinema

Transmission & coding

Remote sensing &

Robot vision

Image processing architectur

e

Color processin

g

Video processin

g

Medical Field Application

The common applications of DIP in the field of medical isGamma ray imagingPET scanX Ray ImagingMedical CTUV imaging

DIGITAL CINEMA

TRANSMISSION AND ENCODINGTRANSMISSION- This the process of communication used for the

transmission of images. For transmission there are many ways available(internet

,fax ,printer etc.)

ENCODING- By the encoding the image is converted in the form

which can be transmitted.

REMOTE SENSING AND ROBOT VISION

HURDEL DETECTION

REMOTE SENSING

IMAGE PROCESSING ARCHITECTURE

COLOR PROCESSINGColor processing includes

processing of colored images and different color spaces that are used. For example RGB color model, CMY, HSI. It also involves transmission, storage, and encoding of these color images

VIDEO PROCESSINGA video is nothing but just the very fast movement of pictures. The quality of the video depends on the number of frames/pictures per minute and the quality of each frame being used. Video processing involves noise reduction, detail enhancement, motion detection, frame rate conversion, aspect ratio conversion, color space conversion etc.

FUTURE SCOPE