Image Filters Panel
Filter image using different image filters.
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Contents
1. Image Filter combo box.
Allows selection of a filter from a list of 2D and 3D image filters. Depending on the filter type some additional parameters should be specified in the HSize, Sigma, lambda, Type, Angle, Iter edit boxes (2). List of available filters:
- Gaussian, (2D) Matlab a rotationally symmetric Gaussian lowpass filter, see more in the Matlab documentation for fspecial and imfilter.
- Gaussian 3D, (3D) is based on Dirk-Jan Kroon implementation and uses the fact that a Gaussian kernel can be implemented as several 1D kernels.
- Perona Malik anisotropic diffusion, (2D) - a filter written by Peter Kovesi to perform anisotropic diffusion of an image following Perona and Malik's algorithm. This process smoothes the regions while preserving, and enhancing the contrast at sharp intensity gradients.
- Average, (2D) Matlab Averaging filter, see more in the Matlab documentation for fspecial and imfilter.
- Disk, (2D) Matlab circular averaging filter (pillbox), see more in the Matlab documentation for fspecial and imfilter.
- Gradient, (2D) generates gradient image for the shown orientation.
- Gradient, (3D) generates gradient image for the whole volume.
- Motion, (2D) Matlab filter to approximate the linear motion of a camera, see more in the Matlab documentation for fspecial and imfilter.
- Unsharp, (2D) Matlab sharpens image using unsharp masking (imsharpen function, R2013a and above) or unsharpens contrast enhancement filter (fspecial and imfilter, R2012b and older).
- Median 2D, (2D) Matlab 2D median filter. Median filtering is a nonlinear operation often used in imageprocessing to reduce "salt and pepper" noise. A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges. See more in the Matlab documentation for medfilt2.
- Wiener 2D, (2D) Matlab 2D 2-D adaptive noise-removal filtering (wiener2 function). wiener2 lowpass-filters a grayscale image that has been degraded by constant power additive noise. wiener2 uses a pixel wise adaptive Wiener method based on statistics estimated from a local neighbourhood of each pixel.
- Edge Enhancing Coherence Filter, (2D and 3D) based on the Image Edge Enhancing Coherence Filter Toolbox written by Dirk-Jan Kroon and Pascal Getreuer. Check here for for details.
- Diplib filters - [optional, requires additional installation ] a number of algorithms that are provided with DIPlib library. Note, to work properly the toolbox should be installed. See details in the System Requirements.
Note! If HSize is specified with a single number then the size of the 3D Kernel is calculated based on pixel size of the dataset Menu->Dataset->Parameters. If HSize is specified with 2 numbers (i.e. 3;3) then the Kernel size is [3 x 3 x 3].
2. Options for the filters
Here is a set of edit boxes that define additional parameters for the filters. Depending on the selected filter (1) one or more of these edit boxes may be disabled.
3. 'All' check box
When checked the filtering is done for all layers of the dataset, otherwise for the shown slice only.
4. The Filter button
Press this button to start the filtering.
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