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 = | = New Features and Classes in ITK = | ||
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::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
- 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::OPDBasedWidefieldMicroscopePointSpreadFunctionImageSource
- 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.)
