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Dr. Chittaranjan Verma delivered this lecture for Digital Image Processing course at B R Ambedkar National Institute of Technology. It includes: Spatial, Filtering, Basics, Digital, Image, Processing, Value, Smoothing, Filters
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Smoothing spatial filters ^ For blurring/noise reduction; ^ Blurring is usually used in preprocessing steps, e.g.,to remove small details from an image prior to objectextraction, or to bridge small gaps in lines or curves ^ Equivalent to Low-pass spatial filtering in frequencydomain because smaller (high frequency) details areremoved
based
on^
neighborhood
averaging
(averaging filters) Applications:
Reduce noise; smooth false contours
^ Side effect:
Edge blurring
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Original imageSize: 500x
Smooth by 3x3box filter
Smooth by 5x
box filter
Smooth by 9x9box filter
Smooth by 15x15 box filter
Smooth by35x35 box filter
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Median filtering
Median =? 20
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Median filtering: Example
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Sharpening Spatial Filters ^ Operation of Image Differentiation^
^ Mathematical
Basis
of^
Filtering
for^
Image
Sharpening ^ First-order and second-order derivatives ^ Approximation in discrete-space domain ^ Implementation by mask filtering
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First and second order derivative
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Comparison of first (f’) andsecond order (f’’) derivative ^ First order derivatives produce thicker edges in an image ^ Second order derivatives have a stronger response tofine details, such as thin lines and isolated points
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Comparison of first (f’) andsecond order (f’’) derivative ^ First
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Comparison of first (f’) andsecond order (f’’) derivative ^ For image enhancement, second order derivative isgenerally better suited than first order derivative ^ Major application of first order derivative is for edgeextraction ^ First
order
derivative
used
together
with
second
order derivative results in impressive enhancementeffect
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Enhancement by second orderderivative: Example Applying the second derivative through 1-D kernel: -1 2 -1On a sequence
2 2 2 2 2 4
6 6 6 6 6
Will give
0 0 0 0 -2 0 +2 0 0 0 0
Adding them
2 2 2 2 0 4
8 0 0 0 0
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Use of second derivatives for imageenhancement: The Laplacian
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Use of second derivatives for imageenhancement: The Laplacian
