![]() ![]() Be careful not to run out of memory when processing large 3D images. Start with the default values and set iterations to 10 initially. For a 2D image, use a 2D (single plane) PSF. Run the Iterative Deconvolve 3D plugin, then select the image and PSF. 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. Try to match the parameters used to capture the raw image. The desired values will need to be empirically determined. The width, height and depth values are for the PSF image, not your image stack. A dialog will appear most of the fields are self explanatory. ![]() To use, run the “Diffraction PSF 3D” plugin. These PSFs may be used with other deconvolution plugins later. The Diffraction PSF 3D plugin can be used to generate theoretical PSFs assuming they arise only from diffraction. See the plugins’ homepages for more details: Diffraction PSF 3D & Iterative Deconvolution 3D Generating a PSF image stack The image below is a single slice taken from a stack before and after deconvolution using these plugins. feature size in your sample image z-stack. The Iterative Deconvolution 3D plugin uses a PSF image z-stack to correct the image contrast vs. Alternatively, an empirical, measured PSF could be used. The Diffraction-PSF-3D plugin generates a z-stack of the theoretical point-spread function (PSF). Two plugins from Bob Dougherty can be used together to perform this systematic error correction in a 2D or 3D image. It’s the same as zeroing a scale before weighing something. If we don’t correct this systematic error, the results of the image intensity analysis could be very much more wrong than if we correct the images before analysis. Image contrast restoration by deconvolution is an important systematic error correction step for quantitative measurement of image pixel intensities in analysis workflows. We try to reverse the effects of blur in the recorded image, caused by convolution (blur, smearing, loss of contrast of small features) of the real image due to the imaging point spread function (PSF). Image contrast restoration by deconvolution is a way to correct the systematic error of contrast loss in an image recording system, such as a microscope objective lens or telescope mirror or lens. Since it’s possible to correct such a systematic error, we should! If we can measure the PSF, or guess it, we can correct the raw image for it. This is a systematic error, characterized by the Point Spread Function (PSF) of the optical system, which makes the image intensity information non-quantitative. Large features are bright, but small features appear less contrasted and dimmer than they should. The problem, and the solutionĪny optical image forming system, such as a microscope objective lens, has the nasty property of killing more and more contrast of smaller and smaller features, up to the resolution (diffraction) limit, after which there is no contrast (and thus no resolution). ![]() (2012), " Fiji: an open-source platform for biological-image analysis", Nature methods 9(7): 676-682, PMID 22743772, doi: 10.1038/nmeth.2019, ( on Google Scholar).Deconvolution corrects the systematic error of blur (loss of contrast in smaller features) in optical systems such as fluorescence microscopy images. If you'd like to share an idea or project, please share them with the community.
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