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StarkParker.java
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Thu, Jul 11, 00:40

StarkParker.java
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/*
* DeconvolutionLab2
*
* Conditions of use: You are free to use this software for research or
* educational purposes. In addition, we expect you to include adequate
* citations and acknowledgments whenever you present or publish results that
* are based on it.
*
* Reference: DeconvolutionLab2: An Open-Source Software for Deconvolution
* Microscopy D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz,
* R. Guiet, C. Vonesch, M Unser, Methods of Elsevier, 2017.
*/
/*
* Copyright 2010-2017 Biomedical Imaging Group at the EPFL.
*
* This file is part of DeconvolutionLab2 (DL2).
*
* DL2 is free software: you can redistribute it and/or modify it under the
* terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* DL2 is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
* A PARTICULAR PURPOSE. See the GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along with
* DL2. If not, see <http://www.gnu.org/licenses/>.
*/
package deconvolution.algorithm;
import java.util.concurrent.Callable;
import signal.ComplexSignal;
import signal.Constraint;
import signal.Operations;
import signal.RealSignal;
import signal.SignalCollector;
public class StarkParker extends AbstractAlgorithm implements Callable<RealSignal> {
private double gamma = 1.0;
public StarkParker(int iterMax, double gamma) {
super();
this.iterMax = iterMax;
this.gamma = gamma;
}
@Override
// Landweber algorithm
// X(n+1) = X(n) + g*H*(Y-H*X(n))
// => X(n+1) = X(n) - g*H*H*X(n) + g*H*Y
// => X(n+1) = X(n) * (I-g*H*H) + g*H*Y
// => pre-compute: A = (I-g*H*H) and G = g*H*Y
// => Iteration : X(n+1) = X(n) * A + G with F(0) = G
public RealSignal call() {
ComplexSignal Y = fft.transform(y);
ComplexSignal H = fft.transform(h);
ComplexSignal A = Operations.delta(gamma, H);
ComplexSignal G = Operations.multiplyConjugate(gamma, H, Y);
SignalCollector.free(H);
SignalCollector.free(Y);
ComplexSignal X = G.duplicate();
controller.setConstraint(Constraint.Mode.CLIPPED);
while (!controller.ends(X)) {
X.times(A);
X.plus(G);
}
SignalCollector.free(A);
SignalCollector.free(G);
RealSignal x = fft.inverse(X);
SignalCollector.free(X);
return x;
}
@Override
public String getName() {
return "Bounded-Variable Least Squares";
}
@Override
public String[] getShortnames() {
return new String[] {"BVLS", "SP"};
}
@Override
public int getComplexityNumberofFFT() {
return 3 + iterMax * 2;
}
@Override
public double getMemoryFootprintRatio() {
return 9.0;
}
@Override
public boolean isRegularized() {
return false;
}
@Override
public boolean isStepControllable() {
return true;
}
@Override
public boolean isIterative() {
return true;
}
@Override
public boolean isWaveletsBased() {
return false;
}
@Override
public AbstractAlgorithm setParameters(double... params) {
if (params == null)
return this;
if (params.length > 0)
iterMax = (int) Math.round(params[0]);
if (params.length > 1)
gamma = (float) params[1];
return this;
}
@Override
public double[] getDefaultParameters() {
return new double[] { 10, 1 };
}
@Override
public double[] getParameters() {
return new double[] { iterMax, gamma };
}
@Override
public double getRegularizationFactor() {
return 0.0;
}
@Override
public double getStepFactor() {
return gamma;
}
}

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