Filter3D provides a set of three-dimensional filters, operations which for each pixel in the output calculate an intensity based on the input intensities in a box-shaped region about the pixel. The same filters are available as in Filter 2D, including mean and median filters. The size of the region is set by the XY and Z kernel size fields.
Filter 3D's user interface uses the same set of controls for selecting and processing a region as other Priism applications.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
Priism | Filter 2D | Convolution
The available filter types are listed below. More details and examples of the results on two-dimensional data are in the Filter 2D documentation.
The median filter looks at the image intensities within a box-shaped region around each pixel and selects the median value for the resulting image. When the enhance toggle is on, the result is the input times a scale factor minus the selected median.
The mean filter is similar to the median filter except that the mean value is used instead of the median.
When the enhance toggle is off, the Gaussian filter replaces a value with the weighted average of it and the neighboring pixels; the weights fall off exponentially as the square of the distance divided by 2 times the square of sigma. When the enhance toggle is on, the result is the input times a scale factor minus the weighted average.
The Laplacian filter implemented here is an approximation to the negative of the Laplacian operator. The Laplacian operator has the useful property that a location where the output values change sign marks the position of an edge. The Laplacian operator involves the computation of differences and therefore tends to amplify noise. If sigma is greater than zero, the Laplacian is combined with a Gaussian smoothing step to give what is known as the Laplacian of Gaussian or LoG filter; this is useful for reducing the amount of noise introduced.
When the Enhance toggle is on, the scale factor times the input minus the result of the of the filter is computed. This is one way to enhance edges in an image; another, likely better, method is to use the EdgeEnh application.
When the enhance toggle is off, the output is an unbiased estimate of the average absolute deviation from the local mean. When the enhance toggle is on, the output is the average absolute deviation from the input intensity at that point.
See the Filter 2D documentation for how this filter is defined - the name is probably a misnomer but it is what was used in previous versions of Filter 2D. If you know a use or a rationale for this particular filter, let the Priism maintainers know.
When the enhance toggle is off, the minimum filter replaces a value with the minimum value in the neighborhood. If the input is a binary image (values are either one or zero), the minimum filter is equivalent to the morphological erosion operator with a square structuring element. When the enhance toggle is on, the result is the input times a scale factor minus the neighborhood minimum.
When the enhance toggle is off, the maximum filter replaces a value with the maximum value in the neighborhood. If the input is a binary image (values are either one or zero), the maximum filter is equivalent to the morphological dilation operator with a square structuring element. When the enhance toggle is on, the result is the input times a scale factor minus the neighborhood maximum.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
When the Enhance toggle is on the result for each iteration is the input data for that iteration times a scale factor minus what the result would be when the toggle was off. The average deviation filter is an exception to this.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
If the Enhance toggle is on and the filter is not an average deviation filter, the result of an iteration is the input data for that iteration times the scale factor minus the filtered input data.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
For the Gaussian and Laplacian (LoG) filters, the width is controlled by the value of sigma in a given dimension. Larger values of sigma give broader filters. The values of sigma for the x and y directions are the same; in the z direction the value of sigma can be given a different value.
When the autosize toggle is on, any changes to the sigma values will automatically calculate a kernel size to give a reasonable approximation for the filter.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
The kernel size is the size, in pixels, of the box used to calculate the filtered images. The box has a square cross section in x and y; the square has a side length given by the value in the XY kernel size field. The height of the box is given by the value in the Z kernel size field.
Overview | Region processing | Method (filter types) | Enhance | Scale factor | Sigma | Kernel size | Iterations
The filter is applied repeatedly with the output from the previous pass becoming the input for the next. The number of passes is shown in the Iterations field.
Overview | Region processing | Method (filter types) | Enhance | Sigma | Kernel size | Iterations