Image Filters dialog
collection of image filters arranged into 4 categories:
 Basic image filtering in the spacial domain
 Edgepreserving filtering
 Contrast adjustment
 Image binarization
A demonstration is available in the following video:
https://youtu.be/QZU3jSoEXJM
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Contents
Options
Prior filtering of images the following options may be tweaked:
 Dataset type  specify whether the image filtering should be done for the shown slice, current 3D stack of the whole dataset
 Source layer  allows to select a source layer which will be filtered
 Color channel  list of existing color channels. It is possible to select a specific color channel, all shown color channels or just all color channels of the image
 Material index  index of material to filter, only when Source layer is Model
 3D  check to apply 3D filter
The filtered image may additionally be postprocessed (a dropdown at the bottom of the dialog window) as
 Filter image  filter image and display it as result of the operation
 Filter and subtract  filter image and subtract the result from the unfiltered image
 Filter and add  filter image and add the result to the unfiltered image
Basic image filtering in the spacial domain
table with the list of available filters
Average filter average image signal using a rectanlular filter; the filtering is done with imfilter function and the "average" predefined filter from fspecial 
2D/3D  
Circular averaging filter (pillbox) average image signal using a diskshaped filter; the filtering is done with imfilter function and the "disk" predefined filter from fspecial 
2D  
Elastic distortion filter Elastic distortion filter, based on Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis by Patrice Y. Simard, Dave Steinkraus, John C. Platt (link) and codes available: stackoverflow Elastic Distortion Transformation by David Franco 
2D  
Entropy filter Local entropy filter, returns an image, where each output pixel contains the entropy (sum(p.*log2(p), where p contains the normalized histogram counts) of the defined neighborhood around the corresponding pixel, see details in entropyfilt 
2D  
Frangi filter Frangi filter to enhance elongated or tubular structures using Hessianbased multiscale filtering The filtering is done with fibermetric 
2D/3D  
Gaussian smoothing filter Rotationally symmetric Gaussian lowpass filter of size (Hsize) with standard deviation (Sigma). The 2D filtering is done with imgaussfilt and 3D with imgaussfilt3 
2D/3D  
Gradient filter Calculate image gradient The filtering is done with gradient function and the acquired X,Y,Z components are converted to the resulting image as sqrt(X^{2} + Y^{2} + Z^{2}) 
2D/3D  
Laplacian of Gaussian filter Filter the image using the Laplacian of Gaussian filter, which highlights the edges The resulting image is converted to unsigned integers by its multiplying with the NormalizationFactor and adding half of max class integer value. The filtering is done with imfilter function and the "log" predefined filter from fspecial 
2D/3D  
Motion filter the filtering is done with imfilter function and the "motion" predefined filter from fspecial 
2D  
Prewitt filter Prewitt filter for edge enhancement the filtering is done with imfilter function and the "prewitt" predefined filter from fspecial 
2D/3D  
Range filter Local range filter, returns an image, where each output pixel contains the range value (maximum value  minimum value) of the defined neighborhood around the corresponding pixel. See details in rangefilt 
2D/3D  
Salt and pepper filter Remove salt & pepper noise from image The images are filtered using the median filter, after that a difference between the original and the median filtered images is taken. Pixels that have threshold higher than IntensityThreshold are considered as noise and removed 
2D  
Sobel filter Sobel filter for edge enhancement the filtering is done with imfilter function and the "sobel" predefined filter from fspecial 
2D  
Std filter Local standard deviation of image. The value of each output pixel is the standard deviation of a neighborhood around the corresponding input pixel. The borders are extimated via symmetric padding: i.e. the values of padding pixels are a mirror reflection of the border pixels. See details in stdfilt 
2D 
Edgepreserving filtering
Remove noise while preserve the edges of the objects using one of the following filters
Anisotropic diffusion filter Edge preserving anisotropic diffusion filtering of images with PeronaMalik algorithm The filtering is done with imdiffusefilt 
2D  
Bilateral filter Edge preserving bilateral filtering of images with Gaussian kernels The filtering is done with imbilatfilt 
2D  
DNNdenoise filter Denoise image using deep neural network The filtering is done with denoiseImage 
2D  
Median filter Median filtering of images in 2D or 3D. Each output pixel contains the median value in the specified neighborhood The 2D filtering is done with medfilt2 and 3D with medfilt3 
2D  
Nonlocal means filter The filtering is done with imnlmfilt 
2D  
BMxD filter Filtering image using the blockmatching and 3D collaborative algorithm, please note that this filter is only licensed to be used in nonprofit organizations Please follow the system requirements page on details how to install it. 
2D 
Contrast adjustment
Here the list of filters that are intended to adjust the contrast of images
Add noise filter Add noise to image The filtering is done with imnoise

2D  
Fast Local Laplacian filter Fast local Laplacian filtering of images to enhance contrast, remove noise or smooth image details The filtering is done with locallapfilt 
2D  
Flatfield correction Flatfield correction to the grayscale or RGB image. The correction uses Gaussian smoothing with a standard deviation of sigma to approximate the shading component of the image The filtering is done with imflatfield 
2D  
Local Brighten filter Brighten lowlight image The filtering is done with imlocalbrighten 
2D  
Local Contrast filter Edgeaware local contrast manipulation of images The filtering is done with localcontrast 
2D  
Reduce Haze filter Reduce atmospheric haze The filtering is done with imreducehaze 
2D  
Unsharp mask filter Sharpen image using unsharp masking: when an image is sharpened by subtracting a blurred (unsharp) version of the image from itself The filtering is done with imsharpen 
2D 
Image binarization
The image binarization filters process the image and generate bitmap mask that can be assigned to the selection or mask layers of MIB (use the DestinationLayer dropdown to specify it)
Edge filter Find edges in intensity image; the filtering is done with edge

2D 
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