Migrate from GitHub to SourceForge with this tool. Check out all of SourceForge's recent improvements.


Vit Stancl

Iterative filtering tool

This is a tool for filtering images (2D picture data) with a selected iterative filters.

Command line parameters:
iterative -i file:Image.png::file:psf.pnf -filter filter_name [-steps number] [-silent] [-tile number] [-window name]


  • -i file:Image.png::file:psf.png - Image means input image, psf.png is first point spread function estimation. For the more info abou possible input configuration see MDSTk documentation.
  • -filter filter_name - used filter type. There are two types of filter in the application:
    1. First group are commonly used filters (see bibliography for the further info):
      • lg - Lam-Goodman's version of the Wiener filter [1]
      • rl - Richardson-Lucy algorithm [2]
      • mle/dmle - Maximum likelihood estimation (MLE) without [3] and with [4] noise reduction.
    2. The second group are the same filters used as inner filters to resolve tiles of the original image in the frequency domain. Input image is splitted in the orthogonal tiles, that are solved separately and after the iteration step they are concatenated back to the output image. Possible values (in the same order as before):
      • divlg
      • divrl
      • divmle
      • divdmle

  • -steps number - Number of iteration steps. Each step is solved and output image is stored (if not in the silent mode).
  • -silent - Use silent mode. Do no text output on the screen and do not save partial results.
  • -tile number - When in the tiling mode (dividing version filters), set the tile size. Given number must divide both image sizes without remainder.
  • -window name - When in the tiling mode, set used wondowing function. Possible values are: tukey, hann, cosine. See wiki for more info.

[1] Edmund Y. Lam, Joseph W. Goodman: Iterative Blind Image Deconvolution in Space and Frequency Domains. SPIE Vol: 3650 0277-786X/99
[2] Jean-Baptiste Sibarita: Deconvolution Microscopy. Advances in Biochemical Engineering/Biotechnology, 95, s. 1288-1291, Springer Berlin / Heidelberg 2005
[3] Matthew McAullife: Microscopy - Blind Deconvolution. http://mipav.cit.nih.gov/documentation/HTML%20Algorithms/MicroscopyBlindDeconvolution.html (April 2011)
[4] P. J. Green: Bayesian reconstructions from emission tomography data using a modified EM algorithm. IEEE Transactions on Medical Imaging 9, 1 (1990), s. 84–93.
[5] M. Španěl: MDSTk documentation. http://mdstk.sourceforge.net/documentation.html (December 2010)