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# Image Filters Panel

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## Overview

The Image Filters Panel lets you apply various 2D and 3D image filters to enhance or process your dataset.

MIB in brief: Image filters

Note on updated filters

Image filters have been significantly updated. For the best experience, use the new filters dialog via the New filters button or Menu -> Image -> Image filters.

## Image Filter

Image Filter: select a filter from a list of 2D and 3D options.
Depending on the filter, specify additional parameters in edit boxes like HSize, Sigma, lambda, Type, Angle, and Iter.

List of available filters

- Average (2D): MATLAB averaging filter. See fspecial and imfilter in MATLAB docs.
- Disk (2D): MATLAB circular averaging filter (pillbox). See fspecial and imfilter in MATLAB docs.
- DNN Denoise (2D): Denoises images using a deep neural network (MATLAB R2017b+, requires Neural Network Toolbox and a good GPU).
- Gaussian (2D): MATLAB rotationally symmetric Gaussian lowpass filter. See fspecial and imfilter in MATLAB docs.
- Gaussian (3D): Based on Dirk-Jan Kroon’s imgaussian, using multiple 1D kernels.
- Gradient (2D/3D): Generates a gradient image.
- Frangi (2D/3D): Hessian-based Frangi Vesselness filter for detecting vessels/ridges. Based on Marc Schrijver and Dirk-Jan Kroon’s implementation. References: Frangi 1998, 2001.
- Motion (2D): MATLAB filter approximating camera motion. See fspecial and imfilter in MATLAB docs.
- Median (2D): MATLAB 2D median filter for reducing "salt and pepper" noise while preserving edges. See medfilt2 in MATLAB docs.
- Median (3D): MATLAB 3D median filter (R2017a+). See medfilt3 in MATLAB docs. YouTube demo.
- Perona Malik anisotropic diffusion (2D): Smooths regions while preserving sharp gradients, by Peter Kovesi.
- Unsharp (2D): Sharpens images using unsharp masking (imsharpen, R2013a+) or contrast enhancement (fspecial/ imfilter, R2012b and older).
- Wiener (2D): MATLAB 2D adaptive noise-removal filter (wiener2), using pixel-wise Wiener methods.
- External: BMxD (2D/3D): Optional block-matching and 3D collaborative filtering. Requires separate installation (see System Requirements).

BM3D and BM4D references

- K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Image denoising by sparse 3D transform-domain collaborative filtering", IEEE TIP vol. 16, no. 8, 2007;
- M. Maggioni, V. Katkovnik, K. Egiazarian, A. Foi, "A Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction", IEEE TIP, vol. 22, no. 1,pp. 119-133 2013.

Note

If HSize is a single number, the 3D kernel size is based on pixel size from Menu -> Dataset -> Parameters. If two numbers (e.g., `3;3`), the kernel is 3x3x3.

## Mode

Mode: choose the dataset portion to filter:

- 2D, shown slice: Filters only the current slice.
- 3D, current stack: Filters the current stack.
- 4D, complete volume: Filters the entire dataset.

## Options

Options: select what happens post-filtration:

- Apply filter: Filters and displays the result.
- Apply and add to the image: Filters and adds the result to the original image.
- Apply and subtract from the image: Filters and subtracts the result from the original image.

## Filter settings

Adjust filter parameters with these edit boxes (some may be disabled based on the filter):

- 3D
- Type
- HSize
- lambda
- Sigma
- beta2
- beta3

## Filter

Filter button: starts the filtering process.

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