High Boost Filtering
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Description
High Boost Filtering
High Boost filtering In image processing, it is often desirable to emphasize high frequency components representing the image details without eliminating low frequency components (such as sharpening). The high-boost filter can be used to enhance high frequency component.
High Boost Filtering •
The high-boost filter can be used to enhance high frequency component while still keeping the low frequency components.
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High boost filter is composed by an all pass filter and a edge detection filter (laplacian filter). Thus, it emphasizes edges and results in image sharpener.
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The high-boost filter is a simple sharpening operator in signal and image processing.
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It is used for amplifying high frequency components of signals and images. The amplification is achieved via a procedure which subtracts a smoothed version of the media data from the original one.
Definitions of the components/Keywords:
• In image processing, we can sharpen edges of a image through the amplification and obtain a more clear image. The high boost filtering is expressed in equation form as follows:
Where A is a constant
is the high boost convolution kernel and
Definitions of the components/Keywords: • Unsharp masking filter (High-boost filter) removes the blurred parts and enhances the edges • The high-boost filtering technique can be implemented using the masks given below for
Original Image
Image after sharpening
Give radio buttons to select the mask and the masks are given below • Give a slider to select any one value of sigma ranging from 1 to 2
Step 1:
Mask 1,Sigma =1
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 1 and sigma=1
Step 2:
mask 1, Sigma 1.2
Instruction for the Text to be displayed in the working animator area (DT) • The first fig. should • The original image appear and then when • The resulting image after high boost the slider points at filtering is applied sigma, the second fig. • The filter mask used for filtering is mask should be shown 1 and sigma=1.2 • The text in DT should appear in parallel to the figures
Step 3:
Mask 1, Sigma 1.8
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 1 and sigma=1.8
Step 4:
Mask 2, Sigma 1
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 2 and sigma=1
Step 5:
Mask 2,Sigma 1.2
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 2 and sigma=1.2
Step 6:
Mask 2, Sigma 1.5
Instruction for the animator
Text to be displayed in the working area (DT)
• The first fig. should • The original image appear and then when • The resulting image after high boost the slider points at filtering is applied sigma, the second fig. • The filter mask used for filtering is mask should be shown 2 and sigma=1.5 • The text in DT should appear in parallel to the figures
Step 7:
Mask 2, Sigma 1.8
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 2 and sigma=1.8
Step 8:
Mask 2, Sigma 2
Instruction for the animator • The first fig. should appear and then when the slider points at sigma, the second fig. should be shown • The text in DT should appear in parallel to the figures
Text to be displayed in the working area (DT) • The original image • The resulting image after high boost filtering is applied • The filter mask used for filtering is mask 2 and sigma=2
Smoothing Spatial Filters Smoothing filters are used for blurring and for noise reduction. –
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Blurring is used in preprocessing steps, such as removal of small details from an image prior to object extraction, and bridging of small gaps in lines or curves Noise reduction can be accomplished by blurring
Type of smoothing filtering There are 2 way of smoothing spatial filters Smoothing Linear Filters Order-Statistics Filters
Smoothing Linear Filters Linear spatial filter is simply the average of the pixels contained in the neighborhood of the filter mask. Sometimes called “averaging filters”. The idea is replacing the value of every pixel in an image by the average of the gray levels in the neighborhood defined by the filter mask.
Two 3x3 Smoothing Linear Filters 1
1 9
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Standard average
1 16
1
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4
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Weighted average
5x5 Smoothing Linear Filters
1 25 ?
1
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Smoothing Linear Filters The general implementation for filtering an MxN image with a weighted averaging filter of size mxn is given by the expression
a b
w ( s ,t)f(x s ,y t)
s a t b g (x ,y ) a
b
w ( s ,t)
s a t b
Result of Smoothing Linear Filters Original Image
[3x3]
[5x5]
[7x7]
Order-Statistics Filters Order-statistics filters are nonlinear spatial filters whose response is based on ordering (ranking) the pixels contained in the image area encompassed by the filter, and then replacing the value of the center pixel with the value determined by the ranking result. Best-known “median filter”
Process of Median filter Corp region of neighborhood 10 15 20 Sort the values of 20 100 20 the pixel in our region 20 20 25 In the MxN mask the median is MxN 10, 15, 20, 20, 20, 20, 20, 25, 100 div 2 +1 5th
Result of median filter
Noise from Glass effect
Remove noise by median filter
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