Table of Contents
What do the filters in the filters sub-menu do?
This submenu contains miscellaneous filters and plugin filters that have been installed by the Plugins/Utilities/Install Plugin command. For more information, refer to the Hypermedia Image Processing Reference at http://www.dai.ed.ac.uk/HIPR2/. Click on Index and look up the keywords convolution, Gaussian, median, mean, erode, dilate and unsharp.
Does spatial convolution using a kernel entered into a text area. A kernel is a matrix whose center corresponds to the source pixel and the other elements correspond to neighboring pixels. The destination pixel is calculated by multiplying each source pixel by its corresponding kernel coefficient and adding the results. There is no arbitrary limit to the size of the kernel but it must be square and have an odd width.
Rows in the text area must all have the same number of coefficients, the rows must be terminated with a carriage return, and the coefficients must be separated by one or more spaces. Kernels can be pasted into the text area using the control-V keyboard shortcut. Checking Normalize Kernel causes each coefficient to be divided by the sum of the coefficients, preserving image brightness.
The kernel shown is a 9 x 9 “Mexican hat”, which does both smoothing and edge detection in one operation. Note that kernels can be saved in a text file (using copy (control-C) and paste), displayed as an image using File/Import/As Text Image, scaled to a reasonable size using Image/Adjust/Size, and plotted using the Surface Plot plugin.
This filter uses convolution with a Gaussian function for smoothing.
Sigma (Radius) is the radius of decay to exp(-0.5) ~ 61%, i.e. the standard deviation of the Gaussian (this is the same as in Photoshop, but different from ImageJ versions till 1.38q, where a value 2.5 times as much had to be entered).
Like all ImageJ convolution operations, it assumes that out-of-image pixels have a value equal to the nearest edge pixel. This gives higher weight to edge pixels than pixels inside the image, and higher weight to corner pixels than non-corner pixels at the edge. Thus, when smoothing with very large blur radius, the output will be dominated by the edge pixels and especially the corner pixels (in the extreme case, with a blur radius of e.g. 1e20, the image will be raplaced by the average of the four corner pixels).
For increased speed, except for small blur radii, the lines (rows or columns of the image) are downscaled before convolution and upscaled to their original length thereafter.
The faster and more accurate version of Gaussian Blur in ImageJ 1.38r and later was contributed by Michael Schmid.
Reduces noise in the active image by replacing each pixel with the median of the neighboring pixel values.
Smooths the current image by replacing each pixel with the neighborhood mean. The size of the neighborhood is specified by entering its radius in a dialog box.
This filter does grayscale erosion by replacing each pixel in the image with the smallest pixel value in that pixel's neighborhood.
This filter does grayscale dilation by replacing each pixel in the image with the largest pixel value in that pixel's neighborhood.
Sharpens and enhances edges by subtracting a blurred version of the image (the unsharp mask) from the original. The unsharp mask is created by Gaussian blurring the original image and then multiplying by the “Mask Weight” parameter. Increase the Guassian blur radius sigma to increase contrast and increase the “Mask Weight” value for additional edge enhancement (as for Process/Filters/Gaussian Blur, the “Gaussian Radius” entered in imageJ versions till 1.38q was 2.5 times sigma).
Heighlights edges in the image by replacing each pixel with the neighborhood variance.
Show Circular Masks
Generates a stack containing examples of the circular masks used by the Median, Mean, Minimum, Maximum and Variance filters for various neighborhood sizes.