Image Filters dialog

collection of image filters arranged into 4 categories:

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:

The filtered image may additionally be post-processed (a dropdown at the bottom of the dialog window) as

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 disk-shaped filter; the filtering is done with imfilter function and the "disk" predefined filter from fspecial	2D
	Distance map filter Calculate distance map from seeds provided in the "Source Layer" dropdown. The 2D map is calculated using MATLAB bwdist, while 3D using bwdistsc by Yuriy Mishchenko The Source Layer should be one of these options: "selection", "mask", "material". Four calculations methods are available for the 2D mode, while in the 3D mode only the "euclidean" method is used.	2D/3D
	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 Hessian-based 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(X2 + Y2 + Z2)	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
	Mathematical operations Apply standard mathematical operations: add, subtract, multiply, or divide to the image. During the operation it is possible to convert image class to a different type.	2D
	Mode filter (R2020a or newer) the filtering is done with modefilt function. Each output pixel contains the mode (most frequently occurring value) in the neighborhood around the corresponding pixel in the input image	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

Edge-preserving 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 Perona-Malik 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
	Non-local means filter The filtering is done with imnlmfilt	2D
	BMxD filter Filtering image using the block-matching and 3D collaborative algorithm, please note that this filter is only licensed to be used in non-profit 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 Several noising schemes are available: gaussian - Gaussian white noise poisson - Poisson noise from the data salt & pepper - adds salt and pepper noise speckle - multiplicative noise using the equation J = I+n*I, where n is uniformly distributed random noise with mean 0 and variance 0.05.	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
	Flat-field correction Flat-field 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 low-light image The filtering is done with imlocalbrighten	2D
	Local Contrast filter Edge-aware 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 Several edge detection schemes are available: approxcanny - finds edges using an approximate version of the Canny edge detection algorithm that provides faster execution time at the expense of less precise detection Canny - finds edges by looking for local maxima of the gradient of I. The edge function calculates the gradient using the derivative of a Gaussian filter. This method uses two thresholds to detect strong and weak edges, including weak edges in the output if they are connected to strong edges. By using two thresholds, the Canny method is less likely than the other methods to be fooled by noise, and more likely to detect true weak edges log - finds edges by looking for zero-crossings after filtering I with a Laplacian of Gaussian (LoG) filter Prewitt - finds edges at those points where the gradient of I is maximum, using the Prewitt approximation to the derivative Roberts - finds edges at those points where the gradient of I is maximum, using the Roberts approximation to the derivative Sobel - Finds edges at those points where the gradient of the image I is maximum, using the Sobel approximation to the derivative	2D
	SLIC clustering filter Cluster together pixels of similar intensity using the SLIC (Simple Linear Iterative Clustering) algorithm Options and settings: Cluster size - approximate size of each cluster in pixels Compactness - a value specifying how much the cluster shape is closer to square (100) or extremely flexible (0) ChopX - as computations are memory intense, it is possible to split the dataset into a number of sub-blocks. Use this field to define number of horizontal blocks ChopY - use this field to define number of vertical blocks	2D
	Watershed clustering filter Cluster together pixels based on presence of ridges using the watershed algorithm Options and settings: DestinationLayer - calculation results can be assigned to different layers of MIB ClusterSize - a parameter that defines size of the generated clusters, the larger the value, the bigger the clusters TypeOfSignal - define color of the ridges: for electron microscopy use "black-on-white", while for light microscopy "white-on-black" GapPolicy - cental line of the watershed ridge forms a gap. The gap can be preserved or filled using the neighbouring clusters ResultingShape - produce as result clusters ("clusters") or gaps between the clusters ("ridges")	2D

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