CS430 - S'05
Lecture 05 : 1/27/05
Reading Assignment: 3 Gonzalez/Woods
Today's Topics
Convolution
Spatial Filtering
"Enigmatic"
Image Enhancement in the Spatial Domain
Masking
The following is a series of diagrams showing the basics of "convolution" or also called masking.
A sample image and a mask
Mask in Red
First Position Calculation
Second Postion Calculation
Third Position Calculation
Fourth Position Calculation
Fifth Position Calculation
Sixth Position Calculation
Seventh Position Calculation
Eighth Position Calculation
Ninth Position Calculation
Final Result
But What About These?
Special Cases
First Type Special Case
Second Type Special Case
Extend With Zeros
No Special Cases
Position 1 Calculation
Extend With Zeros
No Special Cases
Position 2 Calculation
Copy Border Pixels
No Special Cases
Position 1 Calculation
Copy Border Pixels
No Special Cases
Position 2 Calculation
Extend With Fixed Gray Level
Smoothing Examples
3 x 3 Mask With Divisor 1
3 x 3 Mask With Divisor 9
5 x 5 Mask With Divisor 25
Difference Between Peppy And Blurred Peppy
Difference Image Equalized
3 x 3 Mask Done Five Times In A Row
An Attempt To Sharpen Blurred Image
Median Filter
Non Calculating Mask
Running A Median Filter
Peppy Low Noise (salt & pepper)
Peppy Low Noise With Smoothing
Peppy Low Noise With Median Filter
Sharpening Spatial Filters
An Example Of 1D Image Derivatives
Another Example Of 1D Image Derivatives
Peppy With Laplacian Filter
Laplacian Equalized
Peppy With 8 Way Laplacian
8 Way Laplacian Equalized
Peppy With Inverted Laplacian
Peppy(left) With High Boost(right)
High Boost Mask
Horizontal Sobel Gradient Mask
Vertical Sobel Gradient Mask
Horizontal Plus Vertical Sobel Gradient Masks
Noisy Peppy With Laplacian Filter