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IMAGE ENHANCEMENT TECHNIQUES

SUBMITTED BY SUBMITTED BY

SAUMEN BARUASAUMEN BARUA

ROLL :361ROLL :361

COMPUTER SCIENCECOMPUTER SCIENCE

ADVISORADVISOR

MR. ANISUR RAHMANMR. ANISUR RAHMAN

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INTRODUCTION

Image enhancement widely used in Image enhancement widely used in computer graphics.computer graphics.

It is the sub areas of image processing.It is the sub areas of image processing. The principle objectives of image The principle objectives of image

enhancement techniques is to process an enhancement techniques is to process an image so that the result is more suitable image so that the result is more suitable than the original image for a specific than the original image for a specific applicationapplication . .

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METHODS FOR IMAGE ENHANCEMENT Image enhancement techniques can be Image enhancement techniques can be

divided into two broad categories: divided into two broad categories:

1.Spatial domain methods .1.Spatial domain methods . 2 Frequency domain methods.2 Frequency domain methods.

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SPATIAL DOMAIN METHODS The term spatial domain refers to the aggregate of The term spatial domain refers to the aggregate of

pixels composing an image. Spatial domain pixels composing an image. Spatial domain methods are procedures that operate directly on methods are procedures that operate directly on these pixels. Spatial Domain processes will be these pixels. Spatial Domain processes will be denoted by the expression , denoted by the expression ,

g(x,y)= T[f(x,y)]g(x,y)= T[f(x,y)]

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POINT PROCESSING

It is the process of contrast enhancement.It is the process of contrast enhancement. It is the process to produced an image of higher It is the process to produced an image of higher

contrast than the original by darkening a particular contrast than the original by darkening a particular level.level.

Enhancement at any point in an image depends Enhancement at any point in an image depends only on the gray level at that point techniques in only on the gray level at that point techniques in this category ore often referred to as point this category ore often referred to as point processing. processing.

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Median and Max/Min filtering

Median filtering is a powerful smoothing Median filtering is a powerful smoothing technique that does not blur the edges technique that does not blur the edges significantlysignificantly . .

Max/min filtering is used where the max or Max/min filtering is used where the max or min value of the neighbourhood gray levels min value of the neighbourhood gray levels replaces the candidate pelreplaces the candidate pel . .

Shrinking and expansion are useful Shrinking and expansion are useful operations especially in two tone images.operations especially in two tone images.

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IMAGE SUBTRACTION

The difference between two images f(x,y) and The difference between two images f(x,y) and h(x,y) are expressed as,h(x,y) are expressed as,

G(x,y)= f(x,y) – h(x,y)G(x,y)= f(x,y) – h(x,y) Is obtained by computing the difference between Is obtained by computing the difference between

all pairs of corresponding pixels from f and h. The all pairs of corresponding pixels from f and h. The key usefulness of subtraction is the enhancement key usefulness of subtraction is the enhancement of difference between images.of difference between images.

One of the most commercially successful and One of the most commercially successful and beneficial uses of image subtraction is in the area beneficial uses of image subtraction is in the area of medical imaging called mask mode of medical imaging called mask mode radiography .radiography .

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HISTOGRAM EQUALIZATION

Histogram equalization is one of the most Histogram equalization is one of the most important parts for any image processingimportant parts for any image processing . .

This technique can be used on a whole This technique can be used on a whole image or just on a part of an image.image or just on a part of an image.

Histogram equalization can be used to Histogram equalization can be used to improve the visual appearance of an image. improve the visual appearance of an image.

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FREQUENCY DOMAIN METHODS

We compute the Fourier transform of the We compute the Fourier transform of the image to be enhanced, multiply the result by image to be enhanced, multiply the result by a filter (rather than convolve in the spatial a filter (rather than convolve in the spatial domain), and take the inverse transform to domain), and take the inverse transform to produce the enhanced image. produce the enhanced image.

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IMAGE SMOOTHING

The aim of image smoothing is to diminish The aim of image smoothing is to diminish the effects of camera noise, spurious pixel the effects of camera noise, spurious pixel values, missing pixel values etc.values, missing pixel values etc.

Two methods used for image smoothing.Two methods used for image smoothing.

neighborhood averaging and edge- neighborhood averaging and edge- preserving smoothing. preserving smoothing.

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Neighbourhood Averaging

Each point in the smoothed image,F(X,Y) is Each point in the smoothed image,F(X,Y) is obtained from the average pixel value in a obtained from the average pixel value in a neighbourhood of (neighbourhood of (xx,,yy) in the input image. ) in the input image.

For example, if we use a 3*3 For example, if we use a 3*3 neighbourhood around each pixel we would neighbourhood around each pixel we would use the mask .Each pixel value is multiplied use the mask .Each pixel value is multiplied by 1/9, summed, and then the result placed by 1/9, summed, and then the result placed in the output image in the output image

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Edge preserving smoothing

An alternative approach is to use An alternative approach is to use median filtering median filtering instead of neighborhood averaginginstead of neighborhood averaging..

Here we set the grey level to be the median of the Here we set the grey level to be the median of the pixel values in the neighborhood of that pixel. pixel values in the neighborhood of that pixel.

The outcome of median filtering is that pixels with The outcome of median filtering is that pixels with outlying values are forced to become more like outlying values are forced to become more like their neighbors, but at the same time edges are their neighbors, but at the same time edges are preserved ,so this also known as preserved ,so this also known as edge preserving edge preserving smoothing.smoothing.

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Image sharpening

The main aim in image sharpening is to The main aim in image sharpening is to highlight fine detail in the image, or to highlight fine detail in the image, or to enhance detail that has been blurred enhance detail that has been blurred

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Conclusion

The aim of image enhancement is to improve the The aim of image enhancement is to improve the information in images for human viewers, or to information in images for human viewers, or to provide `better' input for other automated image provide `better' input for other automated image processing techniquesprocessing techniques

There is no general theory for determining what is There is no general theory for determining what is `good' image enhancement when it comes to `good' image enhancement when it comes to human perception. If it looks good, it is good! human perception. If it looks good, it is good!

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