ASynopsis

On

“ Image Enhancement ”In

MATLAB

Submitted in partial fulfillment of requirements of

CHHATTISGARH SWAMI VIVEKANAND TECHNICAL UNIVERSITYBHILAI (CG)

Bachelor’s Degree

In Information Technology

PROJECT SUBMITTED BY

SAURABH BANERJEE

Department of Information TechnologyRAIPUR INSTITUTE OF TECHNOLOGY, RAIPUR(CG)

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Table of Contents

Introduction Need of this project Advantages System requirements Front End [MATLAB] Limitations

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INTRODUCTION

Image enhancement techniques are used to emphasize and sharpen image features for display and analysis. Image enhancement is the process of applying these techniques to facilitate the development of a solution to a computer imaging problem. Consequently, the enhancement methods are application specific and are often developed empirically, illustrates the importance of the application by the feedback loop from the output image back to the start of the enhancement process and models the experimental nature of the development. In this figure we define the enhanced image as E(r, c). The range of applications includes using enhancement techniques as preprocessing steps to ease the next processing step or as postprocessing steps to improve the visual perception of a processed image, or image enhancement may be an end in itself. Enhancement methods operate in the spatial domain by manipulating the pixel data or in the frequency domain by modifying the spectral components ,Some enhancement algorithms use both the spatial and frequency domains.

Enhancement is also used as a preprocessing step in applications where human viewing of an image is required before further processing. For example, in one application, high-speed film images had to be correlated with a computer-simulated model of an aircraft. This process was labor intensive because the high-speed film generated many images per second and difficult because of the fact that the images were all dark. This task was made considerably easier by enhancing the images before correlating them to the model, enabling the technician to process many more images in one session. Image enhancement is used for postprocessing to generate a visually desirable image. For instance, we may perform image restoration to eliminate image distortion and find that the output image has lost most of its contrast. Here, we can apply some basic image enhancement methods to restore the image contrast. Alternately, after a compressed image has been restored to its "original" state (decompressed), some postprocessing enhancement may significantly improve the look of the image. For example,

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the standard JPEG compression algorithm may generate an image with undesirable "blocky" artifacts, and postprocessing it with a smoothing filter

Spatial domain methods

The value of a pixel with coordinates (x,y) in the enhanced image is the result of performing some operation on the pixels in the neighbourhood of (x,y) in the input image, F.

Neighbourhoods can be any shape, but usually they are rectangular.

Here, image processing functions can be expressed as : g(x; y) = T (f (x; y));

withf (x; y) the input image,g(x; y) the processed image (i.e. the result or

output image) and T(.) an operator on f , defined over some neighbourhoodN of(x; y).ForN we mostly use a rectangular subimage that is centered at(x; y).

N is a1x1 neighbourhood (point-processing)

In this case,N encompasses exactly one pixel. The operatorT then becomes agray-level transformation function, which we express as :

Spatial domain refers to the aggregate of pixels composing an image. s = T (r); (1.2)with r,s the gray-levels off (x; y) andg(x; y). Using this technique, we canachieve some interesting effects like contrast-stretching and bi-level mapping (here an image is converted so that it only contains black and one color white). The trick is to defineT such that it darkens the gray-levels under a certain tresholdk and brightens the gray-levels above this treshold. If the darkening and brightening are constants (black and white), a black-and-white image is produced. Becauses is only dependent on the value (i.e. the gray-level) ofT in 1 pixel, this technique is called ‘point-processing’

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N is a mxm neighbourhood (spatial filtering)In this case,N encompasses a small region. Note that this method isn’t restricted to doing only enhancement, but it can also be used to smoothen images, etc. The general approach is that the value ofg(x; y) is determined by the values off in a predefined neighbourhood (i.e. the mask/filter) of(x; y). Typical values form range from 3 to even 10.The general name for these processes is ‘mask processing’ or ‘filtering’

Frequency domain methods Image enhancement in the frequency domain is straightforward. We simply compute the Fourier transform of the image to be enhanced, multiply the result by a filter (rather than convolve in the spatial domain), and take the inverse transform to produce the enhanced image.

The idea of blurring an image by reducing its high frequency components, or sharpening an image by increasing the magnitude of its high frequency components is intuitively easy to understand. However, computationally, it is often more efficient to implement these operations as convolutions by small spatial filters in the spatial domain. Understanding frequency domain concepts is important, and leads to enhancement techniques that might not have been thought of by restricting attention to the spatial domain.

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Need of this project:

 Image enhancement is the improvement of digital image quality (wanted e.g. for visual inspection or for machine analysis), without knowledge about the source of degradation

Many different, often elementary and heuristic methods are used to improve images in some sense.

Apart from geometrical transformations some preliminary greylevel adjustments may be indicated, to take into account imperfections in the acquisition system. This can be done pixel by pixel, 

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Advantages of the project:

An image enhancement method that reduces speckle noise and preserves edges is introduced

New parameters has been considered for the evaluation of enhancement techniques.

Optimization of various enhancement techniques can be done to reduce computational complexity as much as possible

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Requirement

Software Specifications

Front End : MATLAB 7.0

Operating System : Windows XP

Tool: IMAGE PROCESSING TOOL

Hardware Specifications

Ram : 1 GB

Hard Disk : 80 GB

Processor : 1.6 GHz

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FRONT END

MATLAB stands for Matrix Laboratory. It is a technical computing

environment for high performance numeric computation and visualization.

Typical uses include the following:

Math and computation.

Algorithm development

Data acquisition

Modeling, simulation, and prototyping

Data analysis, exploration, and visualization

Scientific and engineering graphics

Application development, including graphical user interface building

It integrates numerical analysis, matrix computation, signal processing

and graphics in an easy to use environment, where problems and solutions

are expressed just as they are written mathematically. MATLAB allows us

to express the entire algorithm in a few dozen lines, to compute the solution

with great accuracy in a few minutes on a computer, and to readily

manipulate a three dimension display of the result in colour.

In university environments, MATLAB is the standard computational

tool for introductory and advanced courses in mathematics, engineering, and

science. In industry, MATLAB is the computational tool of choice for

research, development, and analysis.

MATLAB is complemented by a family of application specific

solutions called toolboxes.The Image Processing Toolbox is a collection of

MATLAB functions (called M-functions or M-files) that extend the

capability of the MATLAB environment for the solution of digital image

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processing problems. Other toolboxes that sometimes are used to

complement IPT are the Signal Processing, Neural Network, Fuzzy Logic,

and Wavelet Toolboxes. Area in which toolboxes are available include

signal processing, image processing, control system design, dynamic system

simulation, system

identification, neural networks, wavelength communication and others. It

can handle linear, non-linear continuous-time, discrete-time, multivariable

and multirate systems.

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LIMITATIONS:

Complete information about all the schools is not given.

Only important and salient features are discussed in the project.

The detailed account of the fees structure is not covered.

A solitary school can be covered.

The software cannot be afforded by small budget schools.

The faculty and the courses cannot be exactly analyzed and experienced.

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