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Deconvolution
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Definition

In mathematics, deconvolution is an algorithm-based process used to reverse the effects of convolution on recorded data. The concept of deconvolution is widely used in the techniques of signal processing and image processing [wikipedia].

Raw / Deconvolved

Point spread function

The point spread function (PSF) describes the response of an imaging system to a point source or point object [wikipedia].

Benefits

  • Denoising (useful for live imaging)
  • Increase in resolution
  • Tends to simplify image analysis pipelines

Uses

  • Live samples: Allows to acquire noisier images (less laser power, more speed) and still be able to perform image analysis.
  • Fixed samples: Allows for more precise localization of small structures, beneficial in Colocalization & Co-localization & Coloc & Co-Localisation & Colocalisation experiments for example.
  • Noise removal: Makes most image analysis pipelines more robust, as the presence of noise scatters your results
  • Widefield samples: Allows for 3D reconstructions from widefield samples.

Requirements


Please consult the detailed Good Practices page which details the notes below.

Intensity range

DO NOT SATURATE your images !

Pixel (Voxel) Size

An Optimal deconvolution requires good sampling. Consult the SVI Nyquist Calculator to match your acquisition settings to the deconvolution requirements.
Note that if the values are really too small for your sample (ie you observe bleaching during the acquisition), you can increase the voxel size given by SVI in XY by roughly a factor of 2 (closer to Nyquist sampling) without loosing too much deconvolution benefit. Try not to compromise the Z sampling if possible.

2D or 3D

Deconvolution is extremely powerful in 3D. In 2D you will mostly take advantage of the denoising effect, but not necessarily of the increase in resolution that deconvolution can offer.
add explanatory image ?

Range

Deconvolution tends to generate edge artifacts if there are still intensities there (tissue samples, incomplete sample Z acquisition, ... ), so you should acquire:

  • from Black to Black
ADD images (XZ reslice to show incomplete vs complete Z and show images of edge artifacts

or

  • plan to CROP a couple of slices at the top and bottom of your stack and also in XY

Noise

Do not worry too much about noise. Deconvolution is a noise removal technique ! If you observe bleaching, you can decrease the number of averaging steps or the laser power and accept more noise, then let deconvolution work it out.

Bleach

TBD

Confocal case optical slice matching

TBD

Sample Refractive Index Matching

Deconvolution can help correct for spherical aberrations (PSF changing over the depth of the sample) in deep samples, but needs optimal sampling.

To avoid the effects of refractive index mismatch:

  • Work close to the coverslip
  • Choose mounting media and immersion that matches your sample (oil objectives for fixed samples, water objectives for Having a good refractive index match is

Tools

Huygens Remote Manager

The BIOP chosen solution is Huygens Remote Manager and you can follow this tutorial.

Deconvolution Lab

Deconvolution Lab download and more (dataset, litterature) about deconvolution.

CUDA deconvolution

tutorial

TO BE CONTINUED...

Last Author
romainGuiet
Last Edited
Aug 29 2019, 16:22

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