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372 bytes added, 14:45, 10 December 2016
clarify details and add info to descriptions of plugins and their use.
The Diffraction PSF 3D plugin can be used to generate theoretical PSFs assuming they arise only from diffraction. These PSFs may be used with other deconvolution plugins later.
To use, run the "Diffraction PSF 3D" plugin. A dialog will appear; most of the fields are self explanatory. The width, height and depth values are for the PSF image, not your image stack. The desired values will need to be empirically determined. Try to match the parameters used to capture the raw image.
== Constrained Iterative Deconvolution ==
Non negative constrained (non linear), iterative deconvolution algorithms greatly outperform simple inverse filters and Wiener filters on noisy real life fluorescence microscopy (and other) image data.
Run the Iterative Deconvolve 3D plugin, then select the image and PSF. For a 2D image, use a 2D (single plane) PSF. For 3D images, use a 3D PSF (z stack). Start with the default values and set iterations to 10 initially. Be careful not to represent the PSF with a stack or the plugin will run out of memory and terminatewhen processing large 3D images. Crop them if they are too large.
== An interactive Convolution / Deconvolution / Contrast Restoration demo in ImageJ ==
For an educational interactive ImageJ javascript demo of convolution, inverse filtering and image contrast restoration by iterative constrained deconvolution(using the above plugins), see this [ Convolution / Deconvolution / Contrast Restoration demo script]
== Video presentations ==