This program sharpens the edges in the image. The method for sharpening
is done by applying a high pass filter to a cube and adding the filter
results back to the original image. In general, you will sharpen small
features with smaller boxcars and larger features with larger boxcars.
This is the total number of samples in the boxcar.
It must be odd and can not exceed twice the number of samples
in the cube. In general, the size of the boxcar does not
cause the program to operate significantly slower.
This is the total number of lines in the boxcar.
It must be odd and can not exceed twice the number of lines
in the cube. In general, the size of the boxcar does not
cause the program to operate significantly
slower.
Valid minimum pixel value that will be used in boxcar computation. If
a pixel value is less than LOW then it will not be used when
computing boxcar statistics.
Valid maximum pixel value that will be used in boxcar computation. If
a pixel value is greater than HIGH then it will not be used when
computing boxcar statistics.
This is the minimum number of valid pixels which must occur inside the
NxM boxcar for filtering to occur. For example, 3x5 boxcar has 15 pixels
inside. If MINIMUM=10 then the filter will occur if there are 10 or
greater valid pixels. A valid pixel is one that is not special (NULL, LIS,
etc) and is in the range defined by LOW to HIGH.
This option is used to define how special pixels are handled. If the
center pixel of the boxcar is a special pixel it will be propagated
or set to NULL depending on the value of this parameter.
In this example the boxcar size is the default 3 by 3.
GUI Screenshot
Sharpen Gui
Example Gui
Screenshot of GUI with parameters filled in to perform a "sharpen"ing of the input image with the default boxcar of 3x3.
Input Image
Input image
Input image for sharpen
Parameter Name:
FROM
This is the input image for the sharpen example.
Output Image
Output image showing results of the sharpen application.
Output image for sharpen
Parameter Name:
TO
This is the output image for this example of sharpen. Notice the edges of features are sharper. It is the same as running highpass and adding the highpass output back to the input image.
