ITK/Summer v4 2011 Meeting/A2D2/Deconvolution: Difference between revisions

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= How our A2D2 will advance the field (of microscopy) =
* Deconvolution is commonly used to reduce blur in microscopy images
* We provide algorithms to do this and data to evaluate them
= Distribution =
* Code
** New Deconvolution module
** Point-spread function models for microscopy - external module
* Data: MIDAS
* Dependencies
** FFT-based convolution filter (Cory will do, modification of Gaetan's Insight Journal contribution)
= New Features and Classes in ITK =
== Two widefield microscope point-spread function models ==
* itk::MicroscopePointSpreadFunctionImageSource.h
** itk::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
*** itk::GibsonLanniPointSpreadFunctionImageSource
*** itk::HaeberlePointSpreadFunctionImageSource
* itk::OPDBasedWidefieldMicroscopePointSpreadFunctionIntegrand
* itk::BeadSpreadFunctionImageSource
* itk::ScanImageFilter
* itk::SphereConvolutionFilter
== Classes to enable fitting of parametric image models via the registration framework ==
* itk::ParametricImageSource
* itk::ImageToParametricImageSourceMetric
== Four deconvolution algorithms ==
** Wiener filter
** Landweber algorithm
** Richardson-Lucy maximum likelihood estimation
** Parametric semi-blind deconvolution using parametric point-spread function model
= New Features and Classes in ITK =
= New Features and Classes in ITK =


== Two widefield microscope point-spread function models ==
== Two widefield microscope point-spread function models ==
* Gibson-Lanni model
* Haeberle model


=== Specific classes ===
* itk::MicroscopePointSpreadFunctionImageSource.h
* itk::MicroscopePointSpreadFunctionImageSource.h
** itk::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
** itk::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
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* itk::SphereConvolutionFilter
* itk::SphereConvolutionFilter


* Adaptor class to enable fitting of parametric image models via the registration framework
== Classes to enable fitting of parametric image models via the registration framework ==
* itk::ParametricImageSource
* itk::ImageToParametricImageSourceMetric


* 4 deconvolution algorithms
== Four deconvolution algorithms ==
** Wiener filter
** Wiener filter
** Landweber algorithm
** Landweber algorithm
** Richardson-Lucy maximum likelihood estimation
** Richardson-Lucy maximum likelihood estimation
** Parametric semi-blind deconvolution using parametric point-spread function model
** Parametric semi-blind deconvolution using parametric point-spread function model
= Data =
== Estimated size ==
1 GB
== Sharing ==
* Hosting on Kitware MIDAS installation
* Some images contributed as test data
== Images ==
=== Synthetic images of simple objects (50 images) ===
* Spheres and cylinders convolved with example PSF
* Corrupted by various levels of noise
* 2 shapes X 5 different sizes for each shape X 5 different signal-to-noise ratios
=== Measured images of spherical beads (15 images ) ===
* We have widefield images of 100nm, 200nm, and 500nm beads from Dr. Kerry Bloom (size verified with scanning electron microscope)
* 5 images of each bead size
=== Collaborator images (15-30 images) ===
* 5 images and their associated PSFs from each collaborator (Bloom, Weinbert, and Superfine.)

Latest revision as of 01:18, 11 February 2012

How our A2D2 will advance the field (of microscopy)

  • Deconvolution is commonly used to reduce blur in microscopy images
  • We provide algorithms to do this and data to evaluate them

Distribution

  • Code
    • New Deconvolution module
    • Point-spread function models for microscopy - external module
  • Data: MIDAS
  • Dependencies
    • FFT-based convolution filter (Cory will do, modification of Gaetan's Insight Journal contribution)

New Features and Classes in ITK

Two widefield microscope point-spread function models

  • itk::MicroscopePointSpreadFunctionImageSource.h
    • itk::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
      • itk::GibsonLanniPointSpreadFunctionImageSource
      • itk::HaeberlePointSpreadFunctionImageSource
  • itk::OPDBasedWidefieldMicroscopePointSpreadFunctionIntegrand
  • itk::BeadSpreadFunctionImageSource
  • itk::ScanImageFilter
  • itk::SphereConvolutionFilter

Classes to enable fitting of parametric image models via the registration framework

  • itk::ParametricImageSource
  • itk::ImageToParametricImageSourceMetric

Four deconvolution algorithms

    • Wiener filter
    • Landweber algorithm
    • Richardson-Lucy maximum likelihood estimation
    • Parametric semi-blind deconvolution using parametric point-spread function model

New Features and Classes in ITK

Two widefield microscope point-spread function models

  • itk::MicroscopePointSpreadFunctionImageSource.h
    • itk::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
      • itk::GibsonLanniPointSpreadFunctionImageSource
      • itk::HaeberlePointSpreadFunctionImageSource
  • itk::OPDBasedWidefieldMicroscopePointSpreadFunctionIntegrand
  • itk::BeadSpreadFunctionImageSource
  • itk::ScanImageFilter
  • itk::SphereConvolutionFilter

Classes to enable fitting of parametric image models via the registration framework

  • itk::ParametricImageSource
  • itk::ImageToParametricImageSourceMetric

Four deconvolution algorithms

    • Wiener filter
    • Landweber algorithm
    • Richardson-Lucy maximum likelihood estimation
    • Parametric semi-blind deconvolution using parametric point-spread function model

Data

Estimated size

1 GB

Sharing

  • Hosting on Kitware MIDAS installation
  • Some images contributed as test data

Images

Synthetic images of simple objects (50 images)

  • Spheres and cylinders convolved with example PSF
  • Corrupted by various levels of noise
  • 2 shapes X 5 different sizes for each shape X 5 different signal-to-noise ratios

Measured images of spherical beads (15 images )

  • We have widefield images of 100nm, 200nm, and 500nm beads from Dr. Kerry Bloom (size verified with scanning electron microscope)
  • 5 images of each bead size

Collaborator images (15-30 images)

  • 5 images and their associated PSFs from each collaborator (Bloom, Weinbert, and Superfine.)
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