Fast median filter for circle-valued data, for example signals or images describing phase or orientation
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README.md

Circle median filter toolbox (CMF)

This toolbox contains a fast algorithm for median filtering of signals and images with values on the unit circle, for example phase or orientation data. The (arc distance) median filter for an image y with values on the unit circle is given by

where d denotes the arc distance length of two angles, and r, t are the horizontal and vertical "radii" of the filter mask.

The code is a reference implementation (in C++ with Matlab wrappers) of the algorithms described in the paper:

Martin Storath, Andreas Weinmann. Fast median filtering for phase or orientation data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):639-652, 2018 (preprint)

Example

alt tag

Left: A circle-valued image, i.e. every pixel takes its value on the unit circle (or in angular representation a value in (-pi, pi]). The values are visualized as hue component in the HSV color space. Right: Effect of the circle-median filter using a filter mask of size 7 × 7.

Runtime comparison

The time complexity w.r.t. the size of the filter mask is

  • linear for non-quantized data
  • constant for quantized data

Applications

  • Smoothing of phase data, e.g. interferometric SAR images alt tag

  • Smoothing of orientation data, e.g. wind directions

  • Smoothing of vector fields in polar coordinates, e.g. optical flow images

Contents

  • demos: some demos, self explanatory (implemented in Matlab)
  • auxiliary: some useful helper functions (implemented in Matlab)
  • filters: the fast algorithms for median filtering of circle valued data (implemented in C++ with Matlab wrappers)

Installation and usage

  • From Matlab: Run CMF_install.m in the Matlab console and follow the demos
  • From C++: Compile CMF_library.cpp. The relevant functions are medfiltCirc2D and medfiltCirc2DQuant. Their usage is described as comment in the CMF_library.cpp file.

References

How to cite

User applications

  • S. Quan et al. Derivation of the Orientation Parameters in Built-Up Areas: With Application to Model-Based Decomposition. IEEE Transactions on Geoscience and Remote Sensing, 2018
  • H. Salmane et al. A method for the automated detection of solar radio bursts in dynamic spectra. J. Space Weather Space Clim. 2018