Image%20Enhancement

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Image Enhancement
Biomedical Image Analysis Rangaraj M. Rangayyan
course biomedical image processing
vibhor kumar Hannu Laaksonen
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• Topics to be covered
• Convolution mask Operations
• . unsharp masking
• . Sobtracting Laplacian
• 2) High Frequency Emphasis
• 3) Homomorphic filtering for Enhancement
• 4) Adaptive Contrast enhancement

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Convolution Mask Operators - Unsharp masking
The Generalized equation of unsharp masking
is f(m,n) g(m,n) - µg(m,n) ? g(m,n)
blurred image
Is calculated as average of the pixels in the
window taken around the pixel(m,n)
The weight ? can be changed according to desired
effect. For e.g. For a 3X3 convolution mask the
unsharp masking is given by
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Subtracting Laplacian
The degraded image may be expressed in a Taylor
series as
g(x,y,?) g(x,y,0) ? (x,y,t) -
...
Taking k?2g using diffusion model
We get fe g - k??2g For k? 1 and mask
3X3 we get subtracting Laplacian as
Unlike Laplacian subtracting Laplacian maintain
the intensity information while making the image
sharp
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(a)
(b)

1. original lena image
2. Laplacian
3. Unsharp masking
4. subtracting laplacian

(d)
(c)
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High-frequency Emphasis
Highpass filtering are useful in detecting edges
but for enhancing the images it is necessary to
maintain the intensity information.
High-emphasis filter does the image enhance
keeping the intensity information. The
Butterworth high-emphasis filter can termed as
H(u,v)
Filter gain
Frequency
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1. Original shape image
2. the ideal high pass filter
3. The Butter worth highpass filter
4. the Butterworth high-emphasis filter

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Enhancement using Homomorphic filtering
linear filtering and enhancement
inverse transform
transform
input image
filtered image
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• Original Image
• log transform of original image
• Homomorphic filtering including a Butterworth
  high-emphasis filter
• Butterworth high imphasis filter only
• Graphical Models and Image Processing,
  54(3)259-267,May 1992

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Adaptive Contrast Enhancement

• Adaptive-neighborhood contrast enhancement
• non overlapping regions segmentation
• Overlapping regions segmentation

seed fill region growing The region consists of
spatially connected pixels that falls that fall
within the specified gray level devaiation from
seed pixel. every time data is devided into back
ground and foreground pixels The growth tolerance
threshold ? is highly important factor
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Adaptive Contrast Enhancement
contrast enhancment can be done using the formula
fe b (1Ce)/ (1-Ce)
increased contrast
mean background value

• Part of mammogram with a cluster of calcification
  
• adaptive-neighborhood contrast enhancement
• gamma correction
• unsharp masking
• IEEE Transcation on Medical imaging
  11(3)392-406,1992

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• Topics covered
• Convolution mask Operations
• . unsharp masking
• . Sobtracting Laplacian
• 2) High Frequency Emphasis
• 3) Homomorphic filtering for Enhancement
• 4) Adaptive Contrast enhancement