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Biomedical Imaging Group
3D Deconvolution Microscopy
BIG >  3D Deconvolution Microscopy
DECONVOLUTION

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Open Software

» DeconvolutionLab2

» DeconvolutionLab

» PSF Generator

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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, Matlab, 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.

At this stage, DeconvolutionLab2 includes a friendly user interface to run the following algortihms: Regularized Inverse Filter, Tikhonov Inverse Filter, Naive Inverse Filter, Richardson-Lucy, Richardson-Lucy Total Variation, Landweber (Linear Least Squares), Non-negative Least Squares, Bounded-Variable Least Squares, Van Cittert, Tikhonov-Miller, Iterative Constraint Tikhonov-Miller, FISTA, ISTA.

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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

DeconvolutionLab_2.jar

Version 2.1.2 (27.06.2018)

Do not unzip this downloaded file
Installation

ImageJ

Put the file DeconvolutionLab_2.jar in the plugins folder and restart ImageJ or Fiji. Check the menu Plugins » DeconvolutionLab2.

  1. » DeconvolutionLab2 Lab: Start the complete user interface of DeconvolutionLab2
  2. » DeconvolutionLab2 Run: Run headless a deconvolution command given as a macro
  3. » DeconvolutionLab2 Launch: launch the GUI for a deconvolution command provided as a macro

Example of ImageJ macro

	image = " -image synthetic Cube 10.0 1.0 size 200 100 100"
	psf = " -psf synthetic Double-Helix 3.0 30.0 10.0 size 200 100 100"
	algorithm = " -algorithm RIF 0.1000 -out mip MI1 -path home"
	run("DeconvolutionLab2 Launch", image + psf + algorithm + parameters)





	
ImageJ2		





	
Fiji		





	
Matlab		
	
	
Put the DeconvolutionLab_2 into the java folder of Matlab

	
Example of Matlab commands

	
	javaaddpath([matlabroot filesep 'java' filesep 'DeconvolutionLab_2.jar'])
	result = DL2.RIF(rand(40, 40, 30), rand(30, 30, 30), 0.125, '-out mip rand');

			




	
Stand-alone application		
	
		

		Require a Java JDK 1.8 properly installed in the machine. 
		

		Double-click on the DeconvolutionLab_2.jar file 
		to the start the user interface of DeconvolutionLab2.
		

	





	
Java code		
	
		
Use the DeconvolutionLab2 as a Java library

		
Snippet of Java code

		
	RealSignal r = Lab.getImage("fundus.tif");
	RealSignal h = new DirectionalMotionBlur(2, 20, 20).generate(r.nx, r.ny, r.nz);
	Simulation sim = new Simulation(0, 1, 0);
	RealSignal y = sim.run(r, h);
	TikhonovRegularizedInverseFilter trif = new TikhonovRegularizedInverseFilter(0.001);
	RealSignal x = trif.run(y, h);
	Lab.show(x, "TRIF " + i);

			






	
Icy		
	Not yet ready for the centralized distribution on the Icy website





	
Command line interface		
	
		
    
		
  1. To run the user interface of Deconvolutionlab2, enter the following command line in the terminal:
		
    	java -jar DeconvolutionLab_2.jar Lab
    
		
    2. 
		
  2. To run a deconvolution task with a command following this example:
		
    	java -jar DeconvolutionLab_2.jar Run 
	-image synthetic Cube 10.0 1.0 size 200 100 100 
	-psf synthetic Double-Helix 3.0 30.0 10.0 size 200 100 100 intensity 255.0 
	-algorithm RIF 0.1000 -out mip MI1 -path home
    3. 
		

			















Source code, documentation, previous versions



	Source code on GitHub
	git clone https://github.com/Biomedical-Imaging-Group/DeconvolutionLab2





	History
	Status of the development




	Library API
	Java doc




	Version 2.0.0
	DeconvolutionLab_2-2.0.0.jar













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
Version 2		
	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



	

	Drag and drop
	
Drag and drop the input image file (or directory) into the Image bar or in the image table

	

	

	file
	
Select a file containing a z-stack of images (default path is indicated in Path)

	

	

	directory
	
Select a directory containing the list of images (only files which have a name containing the pattern)

	

	

	synthetic
	
Create a synthetic image by choosing its shape, size, and intensity

	

	

	platform
	
Select one of the open images of the platform (ImageJ)

	

	

	active
	
Select the active image window of the platform (ImageJ)

	





▶ PSF



	

	Drag and drop
	
Drag and drop the PSF image file (or directory) into the PSF bar or in the PSF table

	

	

	file
	
Select a file containing a z-stack of images (default path is indicated in Path)

	

	

	directory
	
Select a directory containing the list of images (only files which have a name containing the pattern)

	

	

	synthetic
	
Create a synthetic PSF by choosing its shape, size, and intensity

	

	

	platform
	
Select one of the open images of the platform (ImageJ)

	

	

	active
	
Select the active image window of the platform (ImageJ)

	







▶ Algorithm


    

  • Choose an algorithm
    

    • 

  • Select the parameters
    • 




▶ Path


    

  • Drag and drop a directory used as working directory
    • 

  • Select the current directory as main directory
    • 













 





	

	
Run

	
Run the deconvolution task in headless mode

	


	

	
Launch

	
Start the deconvolution dialog box, then Run the deconvolution task

	


	

	
Batch

	
The current deconvolution command is added in the Batch table (see Advanced tab)

	




	

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
	No 
	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 with a PSF and corruption with Gaussian and Poisson noise





	Convolution
	CONV
	Direct
	Convolution with a PSF (no additive noise)





	Identity
	I
	Direct
	Copy the input into the output





	Non-stabilized Division
	DIV
	Direct
	Division by a PSF in the Fourier domain







Synopsis of the algorithms of DeconvolutionLab2






	


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






Controller




	-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




Preprocessing



	-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




	-norm
	1
	no | value  
	Normalization factor for the PSF



	

Resources



	-fft
	fastest
	academic | jtransforms | fftw2   
	Indicates the FFT library




	-epsilon
	1E-6
	value  
	Machine Epsilon





 


Example



-image synthetic Cube 10.0 1.0 size 200 100 100 intensity 255.0 -psf synthetic Double-Helix 3.0 30.0 10.0 size 200 100 100 intensity 255.0 -algorithm RIF 0.1000 -out mip MI1 -norm 10.0 -path home

	


Deconvolution of a time lapse series


Paul Kefer and Pierre-Alexandre Vidi, Dept of Cancer Biology at the Wake Forest University Baptist Medical Center, have written a ImageJ macro to 
deconvolve a series of frames by the algorithm RIF of DeconvolutionLab2.

	Download the Image Macro: time_lapse_deconvolvution_rif.ijm

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





	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





	
	

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.