Image Acquisitionand Proces

130 Image Acquisition Processing with LabVIEW
5.8 FILTERS
Image Þlters are routines that either suppress or enhance data to meet a particular
speciÞed criterion. Typical Þlter uses include high contrast area enhancement, edge
detection and smoothing.
The two general types of Þlters that you may come across are linear (often referred
to as a convolution) and nonlinear. Linear Þlters are designed to recalculate the value
of the PUI based on its original value and the values of those surrounding it. This
convolution often weights the effect surrounding pixels have on the PUI, usually with
respect to their distance from it (these weightings are reßected in the coefÞcients of
the convolution kernel). An example convolution kernel is shown below:
0 0 0
0 1 0
0 0 0
The effect of this convolution kernel (hereafter referred to as the Þlter) is nothing.
The recalculated value of the PUI is 1 time its original value, added to 0 times the
values of those around it. A more useful example is:
0 1 0
1 1 1
0 1 0
This Þlter changes the value of the PUI to 1 time its original value, plus the sum
of the North, East, South and West pixel values. Note: unless the intensity values
of the used surrounding pixels are zero, the PUI’s value will become higher
(brighter). If the PUI or its surrounding pixels’ intensities sum to a value higher
than the maximum permitted for the image, intensity clipping will occur. A 256
level grayscale image example using the above Þlter could be:
122 127 125 0 1 0
119 120 123 Æ 1 1 1 Æ 607
115 118 120 0 1 0
Image Data Filter New PUI Value
As an intensity value of 607 is forbidden in a 256 level image, the new PUI
value will be clipped to 255 (white), which does not reßect the true nature of the