The following describes each formula included in the Image Filter Formulas section of McIDAS-V.
Clean Image Filter (fill, bline, eline, pdiff, ldiff)
This formula cleans an image by first applying a shot noise filter to each element in the source region, and then locating and replacing bad lines. Each element is compared to the elements on either side and replaced if the values are significantly different. The average value of each line is then compared to the lines above and below and replaced if it is significantly different.
Input Parameter | Definition | |
fill |
'Average': average of surrounding values (default) 'Min' : source dataset minimum value 'Max' : source dataset maximum value |
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bline |
The beginning line in the source image region (default=first line). |
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eline |
The ending line in the source image region (default=last line). |
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pdiff |
The maximum percentage of the product range to allow before a new value for the pixel is derived using the average of two adjacent pixels (default=15). |
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ldiff |
The minimum percentage of the product range to allow before a new value for the pixel is derived using the average of two adjacent pixels (default=15). |
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stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=100). |
Cloud Image Filter (replace, constant)
This formula applies a cloud filter to an image by comparing each element to the elements of another image. Use this filter to mask off a portion of the first source image.
Input Parameter | Definition | |
replace |
The replacement value (default=minimum value in either sdataset) |
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constant |
The additive constant (default=0) |
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stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=255). |
Core Image Filter (brkpoint1, brkpoint2, replace1, replace2)
This formula applies a core filter to two images by comparing elements to breakpoints. The resulting image has only two values.
Discriminate Image Filter (brkpoint1, brkpoint2, brkpoint3, brkpoint4, replace)
This formula applies a discriminate filter to two images by comparing elements in each image to different high and low breakpoints. Use this filter to mask off a portion of the first source image.
This formula applies a gradient filter to the source image.
Input Parameter | Definition | |
stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=100). |
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ddataset = ABSOLUTE(sdatasetn - (sdatasetn+1) ) |
This formula applies a two-dimensional high-pass filter to each element in the source image. It eliminates low-frequency deviations by clustering them around the distribution's midpoint. A sample average is calculated by the program using values in a two-dimensional region around the source element.
Input Parameter | Definition | |
stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=100). |
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ddataset = (sdataset - (sample average ) + (sample midpoint)) |
High Pass Image Filter (radius, leak)
This formula applies a one-dimensional high-pass filter to each element in the source image. It eliminates low-frequency deviations by clustering them around the distribution's midpoint. A sample average is determined using values on either side of the source element and the percentage of filter efficiency.
Input Parameter | Definition | |
radius |
The sample length surrounding the source element; used for sample average (default=50) |
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leak |
The filter efficiency (default=100) |
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stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=100). |
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Equation for each ddataset element: |
ddataset = (sdataset - (sample average) + (sample midpoint) ) |
Hole Image Filter (brkpoint1, brkpoint2)
This formula searches for missing data and fills the holes in the image using the average of the surrounding element values.
Low Pass 2D Image Filter (linecoef, elecoef)
This formula applies a two-dimensional low-pass filter to each element in the source image. It eliminates high-frequency deviations by replacing the source image data values with the average of the values in the sample defined with the line and element coefficients.
Low Pass Image Filter (radius, leak)
This formula applies a one-dimensional low-pass filter to each element of the source image. It eliminates high-frequency deviations by replacing the source image data values with the average of the values on either side of the source element. The sample average is determined using the percentage of filter efficiency.
Merge Image Filter (brkpoint1, brkpoint2, constant)
This formula merges two images by selecting the sdataset1 value for each element if its value is between the specified breakpoints. Otherwise, it selects the sdataset2 value minus the specified constant.
Input Parameter | Definition | |
brkpoint1 |
The low end breakpoint value (default=minimum sdataset value) |
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brkpoint2 |
The high end breakpoint value (default=maximum sdataset value) |
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constant |
The subtractive constant (default=0) |
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stretch |
The stretch applied to the data, either Histogram or Contrast (default=Histogram). |
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BT Min |
The minimum brightness value for calibration (default=0). |
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BT Max |
The maximum brightness value for calibration (default=100). |
Replace Image Filter (New Value, Old Value)
This formula replaces selected elements in the defined region of the source image with the specified replacement value.
Shot Image Filter (bline, eline, pdiff)
This formula cleans an image by applying a shot noise filter to each element in the source image. Each element is compared to the elements on either side and replaced if the values are significantly different.