image enhancement techniques full report

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IMAGE ENHANCEMENT TECHNIQUES

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INTRODUCTION
Image enhancement widely used in computer graphics.
It is the sub areas of image processing.
The principle objectives of image enhancement techniques is to process an image so that the result is more

suitable than the original image for a specific application .
METHODS FOR IMAGE ENHANCEMENT
Image enhancement techniques can be divided into two broad categories:

1.Spatial domain methods .
2 Frequency domain methods.
SPATIAL DOMAIN METHODS
The term spatial domain refers to the aggregate of pixels composing an image. Spatial domain methods are

procedures that operate directly on these pixels. Spatial Domain processes will be denoted by the expression ,
g(x,y)= T[f(x,y)]



POINT PROCESSING
It is the process of contrast enhancement.
It is the process to produced an image of higher contrast than the original by darkening a particular level.
Enhancement at any point in an image depends only on the gray level at that point techniques in this category

ore often referred to as point processing.
 


Median and Max/Min filtering
Median filtering is a powerful smoothing technique that does not blur the edges significantly .
Max/min filtering is used where the max or min value of the neighbourhood gray levels replaces the candidate pel

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

IMAGE SUBTRACTION
The difference between two images f(x,y) and h(x,y) are expressed as,
G(x,y)= f(x,y) “ h(x,y)
Is obtained by computing the difference between all pairs of corresponding pixels from f and h. The key

usefulness of subtraction is the enhancement of difference between images.
One of the most commercially successful and beneficial uses of image subtraction is in the area of medical

imaging called mask mode radiography .

HISTOGRAM EQUALIZATION
Histogram equalization is one of the most important parts for any image processing .
This technique can be used on a whole image or just on a part of an image.
Histogram equalization can be used to improve the visual appearance of an image.

FREQUENCY DOMAIN METHODS
We 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.

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

values etc.
Two methods used for image smoothing.
neighborhood averaging and edge- preserving smoothing.
Neighbourhood Averaging
Each point in the smoothed image,F(X,Y) is obtained from the average pixel value in a neighbourhood of (x,y) in

the input image.
For example, if we use a 3*3 neighbourhood around each pixel we would use the mask .Each pixel value is

multiplied by 1/9, summed, and then the result placed in the output image
Edge preserving smoothing
An alternative approach is to use median filtering instead of neighborhood averaging.
Here we set the grey level to be the median of the pixel values in the neighborhood of that pixel.
The outcome of median filtering is that pixels with outlying values are forced to become more like their

neighbors, but at the same time edges are preserved ,so this also known as edge preserving smoothing.

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

blurred
Conclusion
The aim of image enhancement is to improve the information in images for human viewers, or to provide `better'

input for other automated image processing techniques
There is no general theory for determining what is `good' image enhancement when it comes to human perception.

If it looks good, it is good!



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