Deconvolution Toolkit
ZEN Toolkits

Deconvolution Toolkit Methods for Computational Optical Sectioning

Even the most advanced imaging systems suffer from unavoidable image blur by optical and electronic microscope components. Fortunately, this can be corrected with computational deconvolution methods. The Deconvolution Toolkit provides a collection of such algorithms for life science applications.

  • Reduce blur. Enhance contrast and resolution.
  • Choose from a range of algorithms based on your priorities.
  • Speed up your workflows with multi-GPU support.
  • 30-day free trial
Improve Your Image Quality by Removing Image Blur
Improve Your Image Quality by Removing Image Blur

Improve Your Image Quality by Removing Image Blur

Solve the problem at its root

Image blur is inevitably introduced by the laws of optical physics and the use of electronic components of imaging systems. This reduces image quality metrics such as signal-to-noise ratio, image contrast, axial resolution, and lateral resolution. Applying deconvolution to your images allows you to optimize each of these image quality metrics or, optimally, all of them at once.

Image: Rat cortical primary culture; projection (extended depth of focus) of a 4-channel z-stack; GPU-based deconvolution. Sample courtesy of H. Braun, LSM Bioanalytik GmbH, Magdeburg, Germany

A Range of Algorithms Designed to Fit Your Needs

A Range of Algorithms Designed to Fit Your Needs

Select based on your priorities, from processing speed to uncompromised imaging quality

Several different deconvolution algorithms have been developed. Some methods are fast and sufficient for systems with low levels of blur. When systems present more challenging levels of blur, more sophisticated algorithms are required that use much more extensive computational resources and time. While these can solve more difficult problems and yield superior image results, the processing time can sometimes present too much of a barrier. ZEN Deconvolution Toolkit offers a range of algorithms, enabling you to choose the best fit for your experimental and post-processing needs.

Improve Images from Various Imaging Systems
Improve Images from Various Imaging Systems

Improve Images from Various Imaging Systems

Deconvolution is more than a tool for just widefield microscopy

While deconvolution is traditionally thought of as a tool for widefield fluorescence microscopy, all imaging systems have blur resulting from optical equipment and electronic components. Whether you work with conventional widefield, Apotome, an LSM system or a Lattice Lightsheet 7, all these imaging systems can benefit from including deconvolution into your image processing workflows. For each imaging system, certain recommended algorithms are more suitable and available to use for optimal results.

Easy to Use and Optimized for High Performance Capabilities
Easy to Use and Optimized for High Performance Capabilities

Easy to Use and Optimized for High Performance Capabilities

Improved features automate your workflow

The Deconvolution Toolkit algorithms have been optimized to make it extraordinarily easy-to-use and high performing. ZEN reads the image’s metadata and automatically determines the point spread function (PSF) of the optical system to adjust the optimal settings for deconvolution. Algorithms are accelerated by more than 10-fold by GPU or even mGPU processing. To optimize time-to-results, all deconvolution algorithms run with ZEN Direct Processing during image acquisition. And the lossless data compression algorithm Zstandard (zstd) further accelerates resource-intensive processing.

Image: U2OS cells labeled for mitochondria (TOM20-mCherry) and microtubules​ (Tubulin-GFP) structures before and after Constrained Iterative Deconvolution.

ZEN Deconvolution Toolkit – Basics and Application

How to separate image content and image blur?

The Point Spread Function (PSF)

The Point Spread Function (PSF) – Why noise-free images are physically impossible

When observing any object at the scale of a single wavelength, the resulting image is inevitably blurred. Fortunately, the resulting blurring pattern, the point spread function is similar for every object. It can be directly observed when imaging a fluorescent point source. 


 

The Point Spread Function (PSF)

Why noise-free images are physically impossible

When observing any object at the scale of a single wavelength, the resulting image is inevitably blurred. Fortunately, the resulting blurring pattern, the point spread function is similar for every object. It can be directly observed when imaging a fluorescent point source.

Deconvolution Principle

Deconvolution Principle – Applying the PSF to digital images

Convolution is a mathematical operation where one function is modulated by another. Image blurring can be understood as the convolution of the real-world objects with the PSF. Luckily, this mathematical process can be inverted, and this is what happens during deconvolution. By convolving the blurred image with the inverse of the PSF, we generate a denoised image.

What is Deconvolution?

Applying the PSF to digital images

Convolution is a mathematical operation where one function is modulated by another. Image blurring can be understood as the convolution of the real-world objects with the PSF. Luckily, this mathematical process can be inverted, and this is what happens during deconvolution. By convolving the blurred image with the inverse of the PSF, we generate a denoised image.

Deconvolution Principle

Deconvolution Fine-Tuning – For advanced users

The highest quality deconvolution requires more information about the exact PSF of your system, including sample preparation. ZEN allows you to enter your own experimental PSF, fine-tune optical parameters and PSF by providing information about sample embedding medium, cover glass thickness, PSF depth variance, and many more parameters.

Deconvolution Fine-Tuning

For advanced users

The highest quality deconvolution requires more information about the exact PSF of your system, including sample preparation. ZEN allows you to enter your own experimental PSF, fine-tune optical parameters and PSF by providing information about sample embedding medium, cover glass thickness, PSF depth variance, and many more parameters.
  

How do Deconvolution Methods Differ?

The basic processes of convolution and deconvolution in the context of image blurring and the PSF is the key to understand deconvolution algorithms. Every deconvolution algorithm in ZEN uses some variation of these.
Simple algorithms like deblurring or nearest-neighbor subtract a PSF-generated image blur from the raw image.
The Inverse Filter method directly applies the inverse PSF. Iterative methods apply the forward PSF to approximate the raw image.

Deconvolution Method

Algorithmic Process

Application

Speed

Result Quality

Deblurring

Subtract blur from same image plane

2D data sets

very fast

medium (2D)

Nearest Neighbor

Subtract blur from neighboring planes

3D with limited stack

very fast

low

Inverse Filter

Apply inverse PSF function

Full 3D stack

fast

medium

Fast Iterative (Meinel)

Iterative; Forward PSF on predicted image; Error function

Perfectly symmetric PSFs

medium

good

Fast Iterative (Richardson-Lucy)

Iterative; Forward PSF; Maximum likelihood

Asymmetric PSFs

medium

good

Constrained Iterative

Iterative; Forward PSF; Optimized maximum likelihood

Gold standard

slow

very good

Deconvolution Default Settings and Options

For most relevant imaging systems

Imaging modality

Widefield  

Apotome

Confocal

Lightsheet

Normalization

Auto

Auto

Auto

Auto

Background correction

off

off

off

off

Flicker correction

off

off

off

off

Decay correction

off

off

off

off

Hot pixel correction

off

off

off

off

Constrained Iterative specific defaults

Strength

Auto

NA

Auto

Manual=5

Likelihood

Poisson

NA

Poisson

Poisson

Regularization

Zero order

NA

First order

Zero order

Optimization

Analytical

NA

Line search

Analytical

First esitmate

Input

NA

Mean

Input

Max interactions

40

NA

7

40

Auto stop percentage

0,1

NA

0,1

0,1

Fast Iterative specific defaults

Method

Meinel

NA

Richardson Lucy

Meinel

Regularization

None

NA

None

None

Optimization

None

NA

None

None

Max Interactions

15

NA

50

15

Auto stop percentage

0,1

NA

0,1

0,1

Regularized Inverse Filter specific defaults

Regularization

Zero order

Zero order

Zero order

Zero order

Downloads

  • ZEN Deconvolution & Direct Processing for Life Science Application

    File size: 917 KB
  • A Practical Guide of Deconvolution

    File size: 2 MB

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