image enhancement techniques full report
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.ppt   IMAGE ENHANCEMENT TECHNIQUES.ppt (Size: 161.5 KB / Downloads: 555)



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 .
Image enhancement techniques can be divided into two broad categories:

1.Spatial domain methods .
2 Frequency 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)]


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.


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 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.


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.


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


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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30-09-2010, 01:36 PM

.ppt   4-Image Enhancement.ppt (Size: 1.26 MB / Downloads: 181)
Image Enhancement

Human perception (focus of this discussion)

Machine perception (ocr)

Application specific

Heuristic based: result better than the original image – subjective assessment
Spatial vs frequency domain
smart paper boy
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.doc   imgage enhancement seminar report.doc (Size: 130 KB / Downloads: 148)
1. An Introduction to Image Enhancement
Image enhancement improves the quality (clarity) of images for human viewing.
Removing blurring and noise, increasing contrast, and revealing details are examples of enhancement operations. For example, an image might be taken of an endothelial cell, which might be of low contrast and somewhat blurred. Reducing the noise and blurring and increasing the contrast range could enhance the image. The original image might have areas of very high and very low intensity, which mask details. An adaptive enhancement algorithm reveals these details. Adaptive algorithms adjust their operation based on the image information (pixels) being processed. In this case the mean intensity,
Contrast and sharpness (amount of blur removal) could be adjusted based on the pixel intensity statistics in various areas of the image.
1.1 Defination of Image Enhancement:
The principle objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application.
Image enhancement is one of the most interesting and visually appealing areas of image processing.
1.2 Image enhancement approaches fall into two broad categories:
1.2.1 Spatial domain and intensity transformations
1.2.2 Frequency domain approaches
1.2.1 Spatial domain
• Image plane
• Image processing methods based on direct manipulation of pixels
• Two principal image processing technique classifications
1. Intensity transformation methods
2. Spatial filtering methods
1.2.1a. spatial domain
1. Aggregate pixels composing an image
2. Computationally more efficient and require less processing resources for implementation
1.2.1b spatial domain processes denoted by the expression
1. g(x, y) = T[f(x, y)]
2. f(x, y) is input image
3. g(x, y) is output image
4. T is an operator on f, defined over some neighborhood of f(x, y)
5. T may also operate on a set of images (adding two images)
1.2.1c Neighborhood of a point (x, y)
1. Square or rectangular sub image area centered at (x, y)
2. Typically, the neighborhood is much smaller than the image
3. Center moves over each pixel in the image
4. T is applied at each point to get g at that location
5. Compute the average intensity of the neighborhood
6. Also possible to have neighborhood approximations in the form of a circle
7. The above application is also called spatial filtering
8. Neighborhood may be extracted by a spatial mask, or kernel, or template, or window.
1.2.1d Single pixel neighborhood or point processing techniques
1. Simplest form of T
2. Smallest possible neighborhood of size 1 × 1
g depends only on the value of f at a single point (r, c)
3. Gray-level transformation function of the form
s = T®
4. r and s denote the gray level of f(x, y) and g(x, y)
5. Thresholding
6. Contrast stretching
1.2.1e larger pixel neighborhoods (mask processing or filtering)
1. a neighborhood around (x, y) to determine the value of g(x, y)
2. Neighborhood defined by masks, filters, kernels, templates, or windows (all refer to the same thing)
3. A kernel is a small 2D array whose coefficients determine the nature of the process
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19-07-2011, 10:01 PM

i want full report on image enhancement.
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20-07-2011, 10:08 AM

you can refer these page details of " image enhancement techniques"link bellow
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.ppt   enhancement.ppt (Size: 1.85 MB / Downloads: 90)
Image Enhancement

Are employed to emphasize, sharpen & smooth 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
Operate in the spatial domain, manipulating the pixel data, or in frequency domain, by modifying the spectral components (figure 8.1.2)
Some used both
Type of techniques :-
Point operations – where each pixel is modified according to a particular equation that is not dependent on other pixel values
Mask operations - where each pixel is modified according to the values in a small neighborhood
Global operations – where all the pixel values in the image are taken into consideration
Spatial domain processing include all three but freq domain use global operations
Gray Scale Modification
Also called gray level scaling or gray level transformation, is the process of taking the original gray level values and changing them to improve image
Relates to improving image contrast and brightness
Image contrast is a measure of the distribution and range of gray levels – the difference between the brightest & darkest pixel values and how the intermediate values are arranged
Image brightness refers to the overall average, or mean, pixel value in the image
Mapping Equations
One method to modify gray levels is a mapping equation
mapping equation changes the pixel’s (gray level) values based on a mathematical function that uses brightness values as input
The outputs of the equation are the enhanced pixel values
Mapping is in the category of point operations
Primary operations applied to gray scale image are to compress or stretch it
Compress that are of little interest to us, and stretch where desire more information
When the slope of the line is 0 – 1, gray level compression
If the slope is greater than 1, gray level stretching
See example 8.2.1
Figure 8.2-2 Gray-level Stretching with Clipping at Both Ends. a) The mapping equation, b) the original image, c) the modified image with the stretch gray levels
Histogram Modification
Use similar function which referred to as histogram modification
Focus on histogram shape and range
Histogram with a small spread has low contrast,
histogram with a wide spread has high contrast
Histogram clustered at the high end corresponds to a bright image
Examination of histogram is useful tools, as it contains information of gray level distribution that easy to see the modifications that may improve
Figure 8.2-10 Histogram Stretching with Clipping. a) Original image, b) histogram of original image, c) image after histogram stretching with out clipping, d) histogram of image ©, e) image after histogram stretching with clipping 1% of the values at the high and low ends
Figure 8.2-11 Histogram Shrinking a) Original image, b) histogram of image (a), c) image after shrinking the histogram to the range [75,175], d) histogram of image ©
Figure 8.2-12 Histogram Slide. The original image for these operations is the image from 8.2-11c that had undergone a histogram shrink process. a) the resultant image from sliding the histogram down up 50, b) the histogram of image (a) , c) the resultant image from sliding the histogram down by 50, b) the histogram of image ©.
Histogram equalization is an effective techn for improving the appearance of a poor image
The function is the same as histogram stretch but often provides more visually pleasing results across a wider range of images
Involves probability theory which treat as the probability distribution of gray levels
Histogram equalization process consists 4 steps:
Find the running sum of the histogram values
Normalize the values from step (1) by dividing by the total number of pixels
Multiply the values from step (2) by the maximum gray level value and round
Map the gray level values to the results
See example 8.2.6
Image sharpening deals with enhancing detail information in an image, typically edges and textures
Detail information is typically in the high spatial frequency information, so these methods include some form of highpass filtering
Many image sharpening algorithms consist 3:-
Extract high frequency information
Combine the high frequency image with original image to emphasize image detail
maximizing image contrast via histogram manipulation
High Frequency Emphasis
Using a high boost spatial filter
This mask is convolved with the image & value x determines the amount of low frequency information retained in the resulting image
Value 8 – highpass filter (output image will contain only the edges)
Larger values will retain more of ori image
Less than 8 – negative of ori
High Boost Spatial Filtering a) Original image
b) results of performing a highboost spatial filter with a 3x3 mask and x = 6
c) histogram stretched version of (b) , note the image is a negative of the original,
d) results of performing a highboost spatial filter with a 3x3 mask and x = 8
e) histogram stretched version of (d), note the image contains edge information only
, f) results of performing a highboost spatial filter with a 3x3 mask and x = 12
g) histogram stretched version of (f)

high boost mask can be extended with -1’s and a corresponding increase in the value x
Larger masks will emphasize the edges more (make them wider), and help to mitigate the effects of any noise in original image
If we create NxN mask, value x is NxN-1,
Directional Difference Filters
Similar to high boost filter but emphasize the edges in a specific direction
This filters also called emboss filters, due to the effect they create on the output image
Directional Difference Filters. a) Original image, b) image sharpened by adding the difference filter result to the original image, followed by a histogram stretch,
c) 3x3 filter result with the +1 and -1 in the horizontal direction which emphasizes vertical lines, d) 3x3 filter result with the +1 and -1 in the vertical direction which emphasizes horizontal lines,
e) 7x7 filter result with the +1 and -1 in the horizontal direction which emphasizes vertical lines, d) 7x7 filter result with the +1 and -1 in the vertical direction which emphasizes horizontal lines
Homomorphic Filtering
Digital images are created from optical images
Optical images consist of 3 primary components, lighting & reflectance component
Lighting component results from lighting conditions present when image is captured, & can change as the lighting conditions change
reflectance component results from the way objects in the image reflect light & are determined by properties of object
Many applications it is useful to enhance reflectance component, while reducing the contribution from the lighting component
Homomorphic filtering is a freq domain filtering process that compresses the brightness (from the lighting conditions), while enhancing the contrast (from the reflectance)
Image model is as follows:
I(r,c) = L(r,c) R(r,c)
where L(r,c) represents the contribution of lighting conditions, & R(r,c) represents the contribution of reflectance properties of objects
Assumes that L(r,c) consists of primarily slow spatial changes (low spatial frequencies), & is responsible for overall range of brightness
Assumptions for R(r,c), consists primarily of high spatial frequency information

Consists of 5 steps:
A natural log transform (base e)
the Fourier transform
the inverse Fourier transform, and
the inverse log function – the exponential
Decouple the L(r,c) & R(r,c) components
Puts the image into freq domain
Perform filtering
Inverse transforms (step 2)
Inverse step 1
Unsharp Masking
Used by photographers to enhance image
It sharpens image by subtracting a blurred (lowpass) version of original image
This was accomplished during film development by superimposing a blurred negative onto corresponding film to produce a sharper result
The process is similar to adding a detail enhanced (highpass) version to original
To improve image contrast, include histogram modification as part of unsharp masking enhancement algorithm
Original image is lowpass filtered, followed by histogram shrink
Resultant image is subtracted from original image
Histogram stretch to restore image contrast
Different ranges of histogram shrinking
Image Smoothing

Used to give image softer or special effect, or to mitigate noise effects
For spatial domain is by considering a pixel and its neighbors and eliminating any extreme values with median or mean filters
In freq domain, is accomplished by some form of lowpass filtering
Equivalent convolution mask can be approximated with Moore-Penrose
Some form of average (mean) filters
The coefficients are all positive, unlike highpass filters
Some common spatial convolution masks, where first 2 are standard arithmetic mean filters & last 2 are approximations to Gaussian filters
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i wants detail project and implimentation report on image enhancement with poor lighting by morphological block analysis.

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