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3D Deconvolution Microscopy
BIG >  3D Deconvolution Microscopy
DECONVOLUTION

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» DeconvolutionLab2

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DeconvolutionLab2

The remasterized Java deconvolution tool

DeconvolutionLab2 is freely accessible and open-source for 3D deconvolution microscopy; it can be linked to well-known imaging software platforms, ImageJ, Fiji, ICY (not yet implemented), Matlab (not yet implemented) and it runs as a stand-alone application. The backbone of our software architecture is a library that contains the number-crunching elements of the deconvolution task. It includes the tool for a complete validation pipeline. Inquisitive minds inclined to peruse the code will find it fosters the understanding of deconvolution.

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Reference

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, M. Unser

DeconvolutionLab2 : An Open-Source Software for Deconvolution Microscopy

Methods-Image Processing for Biologists, vol. 115, 2017.

Installation of DeconvolutionLab2

 

Download and installation

Java Stand-alone
DeconvolutionLab_2.jar Download DeconvolutionLab_2.jar, do not unzip it.

  1. Double-click on the DeconvolutionLab_2.jar file
  2. To run Deconvolutionlab2, enter the following command line in the terminal:
    java jar DeconvolutionLab_2.jar help

ImageJ
Download DeconvolutionLab_2.jar, do not unzip it. Put DeconvolutionLab_2.jar in the plugins folder and restart ImageJ.

  1. Plugins > DeconvolutionLab2_Lab: Start the complete user interface of DeconvolutionLab2
  2. Plugins > DeconvolutionLab2_Run: Run headless a deconvolution command given as a macro, if no macro it is run the Lab
  3. Plugins > DeconvolutionLab2_Launch: launch the GUI for a deconvolution command given as a macro, if no macro it is run the Lab

ImageJ2

Fiji

Matlab

Add DeconvolutionLab_2 in the java path

javaaddpath([matlabroot filesep 'java' filesep 'DeconvolutionLab_2.jar'])

then run a specific algorithm

result = DL2.RIF(image, psf, 0.125 , '');


Icy
Not yet implemented
Download and installation
GIT Repository git clone https://c4science.ch/diffusion/2075/deconvolution.git
Status Status of the development
API Java doc
FFT Libraries
JTransforms Visit the page of JTransforms
JTransforms is already included in Fiji and Icy
Get a JTransforms.jar file and put it in the same directory than DeconvolutionLab_2.jar
FFTW FFTW.zip
It is include the FFTW2 dynamic libraries
for Mac OSX, and Windows 32-bits and 64-bits machines, and Linux 32-bits and 64-bits machines.
Download the FFTW.zip folder, unzip it and put it in the same directory than DeconvolutionLab_2.jar

How to use Deconvolution2

 

Image

PSF

Algorithm

Path

Batch

Run

Launch

Algorithms of DeconvolutionLab2

 

Algorithms Shortname Iterative Step Controllable Regularization Wavelets
Deconvolution
Regularized Inverse Filter

Laplacian Regularized Inverse Filter

RIF

LRIF

Direct No Yes
Tikhonov Regularized Inverse Filter TRIF Direct Yes
Naive Inverse Filter

Inverse Filter

NIF

IF

Direct No Yes
Richardson-Lucy RL Iterative No No
Richardson-Lucy Total Variation RLTV Iterative No Yes
Landweber

Linear Least Squares

LW

LLS

Iterative Yes No
Non-negative Least Squares

Landweber+Positivity

NNLS

LW+

Iterative Yes No
Bounded-Variable Least Squares

Spark-Parker

BVLS

SP

Iterative Yes No
Van Cittert VC Iterative Yes No
Tikhonov-Miller TM Iterative Yes Yes
Iterative Constraint Tikhonov-Miller ICTM Iterative Yes Yes
FISTA FISTA Iterative No Yes Haar, Spline
ISTA ISTA Iterative No Yes Haar, Spline
Simulation
Simulation SIM Direct
Convolution CONV Direct
Identity I Direct
Non-stabilized Division DIV Direct

Results of DeconvolutionLab2

 

The results of the deconvolution in terms of image reconstruction are the same than our previous version DeconvolutionLab. DeconvolutionLab2 improves the usability through friendly user-interface and it run on various imaging platform It offers a larger choice a FFT librairies and different ways to cancel the border artefacts. In addition, it allows a scripting for batch processing.

Scripting DeconvolutionLab2

 

The command line of DeconvolutionLab2 consists of a series of arguments that allows a full control of the processing. The command line is written in a single line, space is mostly used as separator. The general format of the argument is:

-keyword [option] parameters

The list of keywords and the options presenting in the following table. The sign | indicate a OR). The default value are written in bold

Keyword Default Options Description
-image file Mandatory Path to a single file (z-stack) usually a TIF or STK file Source of images.
The 3D input data should be a z-stack of images.
-image directory Path to a directory containing 2D images
[pattern
-image synthetic Name and parameters of the shape
[intensity, size, center]>
-image platform Name of the image of the platform (ImageJ or Icy)
-psf file Mandatory Path to a single file (z-stack) used as PSF usually a TIF or STK file Source of PSF.
The 3D PSF data should be a z-stack of images.
-psf directory Path to a directory containing 2D images
[pattern]
-psf synthetic Name and parameters of the shape
[intensity, size, center]
-psf platform Name of the image of the platform (ImageJ or Icy)
-algorithm Mandatory RIF | TRIF | NIF | LW | NNLS | BVLS | RL | RLTV | TM | ICTM | ISTA | FISTA | VC | I | CONV | SIM | DIV

Synonym of the acronym:

RIF = LRIF, NIF = IF, LW = LLS, NNLS = LW+, BVLS = SP, I = ID
Name and parameter of the algorithm
-path current current | path Working directory
Output (several instances of out are possible)
-out stack intact float

Name of the output:

Note that this name is used as title of the window image and as the filename for the storage

Option for dynamic:

intact | rescaled | normalized | clipped

Option for type:

byte | short | float

Mode:

By default the output is shown and saved.

nosave | noshow

Output as a stack of images (TIF)
-out series intact float Output as series of 2D images (slices, XY)
-out mip intact float Output as a maximum-intensity projection
-out ortho intact float Output as a 3 orthogonal views centered around the keypoint
-out planar intact float Outputs as a 2D side-to-side image of all the z-slices
Iterative Controller (every @n iteration, only at the end by default)
-monitor console table console | table | no Selection of the monitoring output
-verbose log log | quiet | prolix | mute Message monitoring
-stats show show | save | no Statistics
-constraint no no | nonnegativity | clipped Spatial constraint on the signal
-residu no no | value Stops when the minimal residu is reached
-time no no | value Limitation of running time
-reference no no | filename Assess the current deconvolved image with the reference image
Computation
-norm 1 no | value Normalization factor for the PSF
-fft fastest academic | jtransforms | fftw2 Indicates the FFT library
-system yes yes | no
-display yes yes | no The final results is displayed
-multithreading yes yes | no
-epsilon 1E-6 value Machine Epsilon
Border
-pad NO NO 0 0 NO | X2 | X23 | X235 | E2 Lateral and axial padding and extension scheme
-apo NO NO UNIFORM | NO | HAMMING | HANN | COSINE | TUKEY | WELCH Lateral and axial apodization window function

Course

 

Material Download Size Description
Slide in PDF (without the animation/video) 3D-Deconvolution-Microscopy.pdf 4.6 Mb Course given in Neubias 2020, February 2017
Restoration logo.zip 0.6 Mb 2D simulation, influence of the PSF shape to restore the original image
Naive Inverse Filter naive-deconvolution.zip 3.3 Mb 2D simulation of the inverse filter
Simulation to check the resolution test-resolution.ijm 0 Mb Macro to generate simulated data and simulated PSF
Simulation to check the spectral effect spectral-analysis.zip 0.7 Mb 2D simulation of fine structures
Hollow bars bars.zip 28 Mb 3D reference, 3D corruputed data and 3D PSF
C-elegans embryo c-elegans.zip 183 Mb 3 fluoresence channels, 3D data and 3D theoretical PSF
Synthetic microtubules microtubules-challenge.zip 63 Mb 3D data and 3D theoretical PSF of a realistic specimen
Drosophila (crop) drosophila-crop.zip 19 Mb Small 3D data and 3D theoritical PSF
Synthetic microtubules real-donut.zip 463 Mb 3D data and 3D estimated PSF of real well-defined objects (donut)

Conditions of use

You'll be free to use this software for research purposes, but you must not transmit and distribute it without our consent. In addition, you undertake to include a citation whenever you present or publish results that are based on it. EPFL makes no warranties of any kind on this software and shall in no event be liable for damages of any kind in connection with the use and exploitation of this technology.

© 2017 EPFL • webmaster.big@epfl.ch • 20.04.2017