java.lang.Object
- java.lang.Thread
- - gov.nih.mipav.model.algorithms.AlgorithmBase
  - - gov.nih.mipav.model.algorithms.AlgorithmMaximumLikelihoodIteratedBlindDeconvolution

All Implemented Interfaces:

java.awt.event.ActionListener, java.awt.event.WindowListener, java.lang.Runnable, java.util.EventListener
```
public class AlgorithmMaximumLikelihoodIteratedBlindDeconvolution
extends AlgorithmBase
```
An implementation of Maximum Likelihood Iterated Blind Deconvolution based on the following papers:
Holmes, T.J., Maximum Likelihood Image Restoration Adapted for Noncoherent Optical Imaging, JOSA-A, 5(5): 666-673, 1988.

Holmes, T., Bhattacharyya, S., Cooper, J., Hanzel, D., Krishnamurthi, V., Lin, W., Roysam, B., Szarowski, D., Turner, J., Light Microscopic Images Reconstructed by Maximum Likelihood Deconvolution, Ch. 24, Handbook of Biological Confocal Microscopy, J. Pawley, Plenum, 1995.

Holmes, T., Liu, Y., Image Restoration for 2D and 3D Fluorescence Microscopy, Visualization in Biomedical Microscopies, A. Kriete, VCH, 1992.

Holmes, T., Blind Deconvolution of Quantum-Limited Noncoherent Imagery, JOSA-A, 9: 1052 - 1061, 1992.

Nested Class Summary
- Nested classes/interfaces inherited from class java.lang.Thread
  java.lang.Thread.State, java.lang.Thread.UncaughtExceptionHandler

Field Summary

Fields
Modifier and Type	Field	Description
`private float[]`	`m_afResolutions`	DOCUMENT ME!
`private int[]`	`m_aiUnits`	DOCUMENT ME!
`private boolean`	`m_bConstraintsInit`	DOCUMENT ME!
`private boolean`	`m_bUpdatePBar`	Flag indicating whether or not to update the progres bar:.
`private boolean`	`m_bUseMicroscopeConstraints`	Boolean to use constraints:.
`private float`	`m_fAxialBandLimit`	DOCUMENT ME!
`private float[]`	`m_fNewRes`	The new resolutions in each dimension if resample is true.
`private float`	`m_fObjectiveNumericalAperature`	numerical aperature of the imaging lens:.
`private float`	`m_fRadialBandLimit`	3).
`private float`	`m_fRadius`	DOCUMENT ME!
`private float`	`m_fRefractiveIndex`	sample index of refraction:.
`private float`	`m_fTanTheta`	DOCUMENT ME!
`private float`	`m_fWavelength`	wavelength of the reflected or fluorescing light:.
`private int`	`m_iArrayLength`	Original data size:.
`private int`	`m_iDimX`	Constraint parameters:.
`private int`	`m_iDimY`	DOCUMENT ME!
`private int`	`m_iDimZ`	DOCUMENT ME!
`private int`	`m_iNumberIterations`	Number of iterations for the deconvolution:.
`private int`	`m_iNumberProgress`	Show the deconvolved image in progress every m_iNumberProgress steps:.
`private ModelImage`	`m_kCalcResult1`	DOCUMENT ME!
`private ModelImage`	`m_kCalcResult2`	DOCUMENT ME!
`private ModelImage`	`m_kCalcResult3`	DOCUMENT ME!
`private ModelImage`	`m_kEstimatedImage`	estimated of the reconstructed image:.
`private ModelImage`	`m_kImageSpectrum1`	Images to store FFT and Calc results.
`private ModelImage`	`m_kImageSpectrum2`	DOCUMENT ME!
`private ModelImage`	`m_kImageSpectrum3`	DOCUMENT ME!
`private ModelImage`	`m_kMirrorImage`	DOCUMENT ME!
`private ModelImage`	`m_kOriginalSourceImage`	source image to be reconstructed!
`private ModelImage`	`m_kPSFImage`	point spread function image:.
`private ModelImage`	`m_kPSFImageCopy`	Used for color image.
`private ModelImage`	`m_kSourceBlue`	DOCUMENT ME!
`private ModelImage`	`m_kSourceGreen`	DOCUMENT ME!
`private ModelImage`	`m_kSourceImage`	source image to be reconstructed!
`private ModelImage`	`m_kSourceRed`	For color images: the source is converted to gray for the deconvolution.

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
destFlag, destImage, image25D, mask, maxProgressValue, minProgressValue, multiThreadingEnabled, nthreads, progress, progressModulus, progressStep, runningInSeparateThread, separable, srcImage, threadStopped

Fields inherited from class java.lang.Thread
MAX_PRIORITY, MIN_PRIORITY, NORM_PRIORITY

Constructor Summary

Constructors
Constructor	Description
`AlgorithmMaximumLikelihoodIteratedBlindDeconvolution(ModelImage kSrcImg, int iIterations, int iProgress, float fObjectiveNumericalAperature, float fWavelength, float fRefractiveIndex, boolean bUseConstraints, int[] newExtents, boolean doResample)`	Creates a new AlgorithmMaximumLikelihoodIteratedBlindDeconvolution object.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`private ModelImage`	`calc(ModelImage kReturn, ModelImage kImage1, ModelImage kImage2, int iType)`	Performs a AlgorithmImageCalculator calculation on the two input images.
`private void`	`cleanUp()`	Dispose of temporary images after the deconvolution process is completed:
`private ModelImage`	`constraints(ModelImage kImagePSF)`	Applies constraints to the input image kImage and writes the constrained values into the m_kPSFImage.
`private ModelImage`	`convertFromGray(ModelImage kRed, ModelImage kGreen, ModelImage kBlue)`	Called after the deconvolution algorithm is run.
`private ModelImage`	`convertToGray(ModelImage kImage)`	Convert the input image, kImage, to a grayscale image using the AlgorithmRGBtoGray.
`private ModelImage`	`convolve(ModelImage kImage1, ModelImage kImage2, float fNoise)`	Convolves two image by computing the fast fourier transforms of each image, multiplying the ffts and then computing the inverse fft of the result:
`void`	`disposeLocal()`	Dispose of local variables that may be taking up lots of room.
`private ModelImage`	`fft(ModelImage kImageFFT, ModelImage kImage, int iDir)`	Computes the fft of the input image, returns the fft.
`void`	`finalize()`	Prepares this class for destruction.
`ModelImage`	`getPSFImage()`	Returns the point spread function image.
`ModelImage`	`getReconstructedImage()`	Returns the reconstructed image:
`private void`	`initConstraints()`	Initializes the constraint variables for the PSF deconvolution, based on the microscope parameters (numerical aperature, wavelength, and refractive index).
`private void`	`initPSF(int iNumberDimensions, int[] aiExtents)`	Initialize the first PSF guess as a 3x3 Gaussian.
`private ModelImage`	`mirror(ModelImage kImage)`	Does a mirror operation on an image, so that x, y, and z values are mirrored Calls JDialogFlip.
`private ModelImage`	`resample(ModelImage kImage, int[] newExtents)`
`void`	`runAlgorithm()`	Starts the program.
`private ModelImage`	`runDeconvolution(ModelImage kSource, ModelImage kEstimate, ModelImage kPSF, boolean bCopy)`	Runs the deconvolution algorithm.

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase
actionPerformed, addListener, addProgressChangeListener, calculateImageSize, calculatePrincipleAxis, computeElapsedTime, computeElapsedTime, convertIntoFloat, delinkProgressToAlgorithm, delinkProgressToAlgorithmMulti, displayError, errorCleanUp, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, fireProgressStateChanged, generateProgressValues, getDestImage, getElapsedTime, getMask, getMaxProgressValue, getMinProgressValue, getNumberOfThreads, getProgress, getProgressChangeListener, getProgressChangeListeners, getProgressModulus, getProgressStep, getProgressValues, getSrcImage, isCompleted, isImage25D, isMultiThreadingEnabled, isRunningInSeparateThread, isThreadStopped, linkProgressToAlgorithm, linkProgressToAlgorithm, makeProgress, notifyListeners, removeListener, removeProgressChangeListener, run, setCompleted, setImage25D, setMask, setMaxProgressValue, setMinProgressValue, setMultiThreadingEnabled, setNumberOfThreads, setProgress, setProgressModulus, setProgressStep, setProgressValues, setProgressValues, setRunningInSeparateThread, setSrcImage, setStartTime, setThreadStopped, startMethod, windowActivated, windowClosed, windowClosing, windowDeactivated, windowDeiconified, windowIconified, windowOpened

Methods inherited from class java.lang.Thread
activeCount, checkAccess, clone, countStackFrames, currentThread, dumpStack, enumerate, getAllStackTraces, getContextClassLoader, getDefaultUncaughtExceptionHandler, getId, getName, getPriority, getStackTrace, getState, getThreadGroup, getUncaughtExceptionHandler, holdsLock, interrupt, interrupted, isAlive, isDaemon, isInterrupted, join, join, join, onSpinWait, resume, setContextClassLoader, setDaemon, setDefaultUncaughtExceptionHandler, setName, setPriority, setUncaughtExceptionHandler, sleep, sleep, start, stop, suspend, toString, yield

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_afResolutions
```
private float[] m_afResolutions
```
    DOCUMENT ME!
  - m_aiUnits
```
private int[] m_aiUnits
```
    DOCUMENT ME!
  - m_bConstraintsInit
```
private boolean m_bConstraintsInit
```
    DOCUMENT ME!
  - m_bUpdatePBar
```
private boolean m_bUpdatePBar
```
    Flag indicating whether or not to update the progres bar:.
  - m_bUseMicroscopeConstraints
```
private boolean m_bUseMicroscopeConstraints
```
    Boolean to use constraints:.
  - m_fAxialBandLimit
```
private float m_fAxialBandLimit
```
    DOCUMENT ME!
  - m_fObjectiveNumericalAperature
```
private float m_fObjectiveNumericalAperature
```
    numerical aperature of the imaging lens:.
  - m_fRadialBandLimit
```
private float m_fRadialBandLimit
```
    3). Bandlimit and missing cone constraint:
  - m_fRadius
```
private float m_fRadius
```
    DOCUMENT ME!
  - m_fRefractiveIndex
```
private float m_fRefractiveIndex
```
    sample index of refraction:.
  - m_fTanTheta
```
private float m_fTanTheta
```
    DOCUMENT ME!
  - m_fWavelength
```
private float m_fWavelength
```
    wavelength of the reflected or fluorescing light:.
  - m_iArrayLength
```
private int m_iArrayLength
```
    Original data size:.
  - m_iDimX
```
private int m_iDimX
```
    Constraint parameters:.
  - m_iDimY
```
private int m_iDimY
```
    DOCUMENT ME!
  - m_iDimZ
```
private int m_iDimZ
```
    DOCUMENT ME!
  - m_iNumberIterations
```
private int m_iNumberIterations
```
    Number of iterations for the deconvolution:.
  - m_iNumberProgress
```
private int m_iNumberProgress
```
    Show the deconvolved image in progress every m_iNumberProgress steps:.
  - m_fNewRes
```
private float[] m_fNewRes
```
    The new resolutions in each dimension if resample is true. Used only if resample is true.
  - m_kCalcResult1
```
private ModelImage m_kCalcResult1
```
    DOCUMENT ME!
  - m_kCalcResult2
```
private ModelImage m_kCalcResult2
```
    DOCUMENT ME!
  - m_kCalcResult3
```
private ModelImage m_kCalcResult3
```
    DOCUMENT ME!
  - m_kEstimatedImage
```
private ModelImage m_kEstimatedImage
```
    estimated of the reconstructed image:.
  - m_kImageSpectrum1
```
private ModelImage m_kImageSpectrum1
```
    Images to store FFT and Calc results.
  - m_kImageSpectrum2
```
private ModelImage m_kImageSpectrum2
```
    DOCUMENT ME!
  - m_kImageSpectrum3
```
private ModelImage m_kImageSpectrum3
```
    DOCUMENT ME!
  - m_kMirrorImage
```
private ModelImage m_kMirrorImage
```
    DOCUMENT ME!
  - m_kOriginalSourceImage
```
private ModelImage m_kOriginalSourceImage
```
    source image to be reconstructed!
  - m_kPSFImage
```
private ModelImage m_kPSFImage
```
    point spread function image:.
  - m_kPSFImageCopy
```
private ModelImage m_kPSFImageCopy
```
    Used for color image.
  - m_kSourceBlue
```
private ModelImage m_kSourceBlue
```
    DOCUMENT ME!
  - m_kSourceGreen
```
private ModelImage m_kSourceGreen
```
    DOCUMENT ME!
  - m_kSourceImage
```
private ModelImage m_kSourceImage
```
    source image to be reconstructed!
  - m_kSourceRed
```
private ModelImage m_kSourceRed
```
    For color images: the source is converted to gray for the deconvolution. Once the estimate and psf are found, each component is reconstructed.
- Constructor Detail
  - AlgorithmMaximumLikelihoodIteratedBlindDeconvolution
```
public AlgorithmMaximumLikelihoodIteratedBlindDeconvolution(ModelImage kSrcImg,
                                                            int iIterations,
                                                            int iProgress,
                                                            float fObjectiveNumericalAperature,
                                                            float fWavelength,
                                                            float fRefractiveIndex,
                                                            boolean bUseConstraints,
                                                            int[] newExtents,
                                                            boolean doResample)
```
    Creates a new AlgorithmMaximumLikelihoodIteratedBlindDeconvolution object.
    
    Parameters:
    
    kSrcImg - the input image to be reconstructed
    
    iIterations - the number of times to iterate in the deconvolution
    
    iProgress - Display deconvolved image every iProgress images
    
    fObjectiveNumericalAperature - the numerical aperature of the imaging lense
    
    fWavelength - the reflected or fluorescing light
    
    fRefractiveIndex - the index of refraction for the sample
    
    bUseConstraints - When true, the lens NA, wavelength, and refractive index are used in the deconvolution process
- Method Detail
  - disposeLocal
```
public void disposeLocal()
```
    Dispose of local variables that may be taking up lots of room.
  - finalize
```
public void finalize()
```
    Prepares this class for destruction.
    
    Overrides:
    
    finalize in class AlgorithmBase
  - getPSFImage
```
public ModelImage getPSFImage()
```
    Returns the point spread function image.
    
    Returns:
    
    m_kPSFImage
  - getReconstructedImage
```
public ModelImage getReconstructedImage()
```
    Returns the reconstructed image:
    
    Returns:
    
    m_kEstimatedImage
  - runAlgorithm
```
public void runAlgorithm()
```
    Starts the program.
    
    Specified by:
    
    runAlgorithm in class AlgorithmBase
  - calc
```
private ModelImage calc(ModelImage kReturn,
                        ModelImage kImage1,
                        ModelImage kImage2,
                        int iType)
```
    Performs a AlgorithmImageCalculator calculation on the two input images. Used here to either multiply or divide two images.
    
    Parameters:
    
    kReturn - DOCUMENT ME!
    
    kImage1 - the first input image
    
    kImage2 - the second input image
    
    iType - the type of calculation (multiply or divide)
    
    Returns:
    
    kReturn, the result of the image calculation
  - cleanUp
```
private void cleanUp()
```
    Dispose of temporary images after the deconvolution process is completed:
  - constraints
```
private ModelImage constraints(ModelImage kImagePSF)
```
    Applies constraints to the input image kImage and writes the constrained values into the m_kPSFImage. Constraints are the following: 2). Hourglass constraint 3). Bandlimit and missing cone constraint 4). Nonnegativity constraint 1). Unit summation constraint
    
    Parameters:
    
    kImagePSF - the unconstrained PSFImage
    
    Returns:
    
    DOCUMENT ME!
  - convertFromGray
```
private ModelImage convertFromGray(ModelImage kRed,
                                   ModelImage kGreen,
                                   ModelImage kBlue)
```
    Called after the deconvolution algorithm is run. The deconvolution processes the grayscale image. Once the estimated and psf images are calculated, they are used to reconstruct the red, green, and blue channels separately, which are then recombined into a color image.
    
    Parameters:
    
    kRed - the reconstructed red image
    
    kGreen - the reconstructed green image
    
    kBlue - the reconstructed blue image
    
    Returns:
    
    the RGB image
  - convertToGray
```
private ModelImage convertToGray(ModelImage kImage)
```
    Convert the input image, kImage, to a grayscale image using the AlgorithmRGBtoGray. The deconvolution is done on the grayscale image, however after deconvolution, the original color values are reconstructed from the psf and grayscale estimate.
    
    Parameters:
    
    kImage - the color image to conver to grayscale:
    
    Returns:
    
    the converted grayscale image
  - convolve
```
private ModelImage convolve(ModelImage kImage1,
                            ModelImage kImage2,
                            float fNoise)
```
    Convolves two image by computing the fast fourier transforms of each image, multiplying the ffts and then computing the inverse fft of the result:
    
    Parameters:
    
    kImage1 - the first input image
    
    kImage2 - the second input image
    
    fNoise - amount of Noise to add when the result of the convolution is going to be the denominator in the next equation to avoid divide by zero errors.
    
    Returns:
    
    kResult, the result of convolving the two images
  - fft
```
private ModelImage fft(ModelImage kImageFFT,
                       ModelImage kImage,
                       int iDir)
```
    Computes the fft of the input image, returns the fft. Calls AlgorithmFFT.
    
    Parameters:
    
    kImageFFT - DOCUMENT ME!
    
    kImage - the input image
    
    iDir - forward fft when iDir = 1, inverse fft when iDir = -1
    
    Returns:
    
    kResult, the fft of kImage
  - initConstraints
```
private void initConstraints()
```
    Initializes the constraint variables for the PSF deconvolution, based on the microscope parameters (numerical aperature, wavelength, and refractive index). The pixel resolutions are also converted to nanometers -- the units for wavelength.
  - initPSF
```
private void initPSF(int iNumberDimensions,
                     int[] aiExtents)
```
    Initialize the first PSF guess as a 3x3 Gaussian.
    
    Parameters:
    
    iNumberDimensions - iNumberDimensions, the dimensions of the data
    
    aiExtents - aiExtents, the ranges in each dimension
  - mirror
```
private ModelImage mirror(ModelImage kImage)
```
    Does a mirror operation on an image, so that x, y, and z values are mirrored Calls JDialogFlip.
    
    Parameters:
    
    kImage - the input image
    
    Returns:
    
    DOCUMENT ME!
  - resample
```
private ModelImage resample(ModelImage kImage,
                            int[] newExtents)
```
  - runDeconvolution
```
private ModelImage runDeconvolution(ModelImage kSource,
                                    ModelImage kEstimate,
                                    ModelImage kPSF,
                                    boolean bCopy)
```
    Runs the deconvolution algorithm.
    
    Parameters:
    
    kSource - the original image data
    
    kEstimate - the initial estimated guess at the reconstructed image
    
    kPSF - the initial point spread function guess
    
    bCopy - when true the new estimates after interating are copied back into the data members.
    
    Returns:
    
    , the resulting estimated image

Class AlgorithmMaximumLikelihoodIteratedBlindDeconvolution

Nested Class Summary

Nested classes/interfaces inherited from class java.lang.Thread

Field Summary

Fields inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Fields inherited from class java.lang.Thread

Constructor Summary

Method Summary

Methods inherited from class gov.nih.mipav.model.algorithms.AlgorithmBase

Methods inherited from class java.lang.Thread

Methods inherited from class java.lang.Object

Field Detail

m_afResolutions

m_aiUnits

m_bConstraintsInit

m_bUpdatePBar

m_bUseMicroscopeConstraints

m_fAxialBandLimit

m_fObjectiveNumericalAperature

m_fRadialBandLimit

m_fRadius

m_fRefractiveIndex

m_fTanTheta

m_fWavelength

m_iArrayLength

m_iDimX

m_iDimY

m_iDimZ

m_iNumberIterations

m_iNumberProgress

m_fNewRes

m_kCalcResult1

m_kCalcResult2

m_kCalcResult3

m_kEstimatedImage

m_kImageSpectrum1

m_kImageSpectrum2

m_kImageSpectrum3

m_kMirrorImage

m_kOriginalSourceImage

m_kPSFImage

m_kPSFImageCopy

m_kSourceBlue

m_kSourceGreen

m_kSourceImage

m_kSourceRed

Constructor Detail

AlgorithmMaximumLikelihoodIteratedBlindDeconvolution

Method Detail

disposeLocal

finalize

getPSFImage

getReconstructedImage

runAlgorithm

calc

cleanUp

constraints

convertFromGray

convertToGray

convolve

fft

initConstraints

initPSF

mirror

resample

runDeconvolution