= 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 [[ https://en.wikipedia.org/wiki/Deconvolution | [wikipedia]]].
{F2292624, name="Raw/Deconvolved" , layout=center, width=512, alt="Raw / Deconvolved"}
= Point spread function=
> The point spread function (PSF) describes the response of an imaging system to a point source or point object [[ https://en.wikipedia.org/wiki/Point_spread_function | [wikipedia]]].
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= Benefits =
-- Denoising (useful for live imaging)
- Increase in resolution
- Denoise (useful for live
- Tends to simplify imaging)
= Requirements =e analysis pipelines
Please consult the detailed [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]] page or read the few notes below.= Uses =
==Intensity range ==
(IMPORTANT) DO NOT SATURATE your images !- 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, for more details consult [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]] pagebeneficial in [[../colocalization]], for example.
== Pixel size ==
(NOTE) An Optimal deconvolution requires a good sampling, please use this [[ https://svi.nl/NyquistCalculator | calculator]] to match your acquisition settings to the deconvolution requirements.
(WARNING) If the values are really too small for your sample (ie you observe bleaching during the acquisition), you could increase the size of the pixel by a factor of 2 in xy (without loosing too much deconvolution benefit).
== Nois Requirements =
Please consult the detailed [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/teaching/good_practices/ | Good Practices ]] page which details the notes below.
==Intensity range ==
(IMPORTANT) DO NOT SATURATE your images !
== Pixel (Voxel) Size ==
(NOTE) Do not worry too much about noise An Optimal deconvolution requires good sampling. Deconvolution is a noise removal technique !
If you observe bleaching,Consult the [[ https://svi.nl/NyquistCalculator | SVI Calculator]] to match your acquisition settings to the deconvolution requirements.
(WARNING) 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. decrease number of averaging and accept some noiseTry not to compromise the Z sampling if possible.
== 2D or 3D ==
(NOTE) Deconvolution is extremely powerfull in 3D but in. In 2D you will mostly take adaadvantage of the denoising effect, but not necessarily of the increase in resolution that deconvolution can offer.
(IMPORTANT) add explanatory image ?
== 3D : z-stack ==
=== Step size ===
Please use this [[ https://svi.nl/NyquistCalculator | calculator]] to match your acquisition settings to the deconvolution requirements.
You better compromise a bit the xy pixel size rather than the z-step.
=== Range ===
Deconvolution could generate border artefacts, so you should acquire:
- from Black to Black
(IMPORTANT) ADD images
or
- plan to CROP a couple of slices at the top and bottom of your stack
(IMPORTANT) crop xy
== Noise ==
(NOTE) Do not worry too much about noise. Deconvolution is a noise removal technique !
If you observe bleaching, decrease number of averaging and accept some noise.
== Bleach ==
(IMPORTANT) TBD
== Confocal case optical slice matching==
(IMPORTANT) TBD
= Tools=
== Huygens Remote Manager ==
The BIOP chosen solution is [[ https://hrm-biop.epfl.ch/ | Huygens Remote Manager]] and you can follow this [[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/deconvolution/hrm/ | tutorial]].
== Deconvolution Lab ==
Deconvolution Lab [[ http://bigwww.epfl.ch/deconvolution/ | download]] and more (dataset, litterature) about deconvolution.
== CUDA deconvolution ==
[[ https://c4science.ch/w/bioimaging_and_optics_platform_biop/image-processing/deconvolution/cuda-deconvolution/ | tutorial ]]
TO BE CONTINUED...