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TikhonovRegularizedInverseFilter.java
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R2075 deconvolution
TikhonovRegularizedInverseFilter.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.Operations
;
import
signal.RealSignal
;
import
signal.SignalCollector
;
import
signal.factory.complex.ComplexSignalFactory
;
public
class
TikhonovRegularizedInverseFilter
extends
AbstractAlgorithm
implements
Callable
<
RealSignal
>
{
private
double
lambda
=
0.1
;
public
TikhonovRegularizedInverseFilter
(
double
lambda
)
{
super
();
this
.
lambda
=
lambda
;
}
@Override
public
RealSignal
call
()
{
if
(
optimizedMemoryFootprint
)
return
runOptimizedMemoryFootprint
();
else
return
runTextBook
();
}
public
RealSignal
runTextBook
()
{
ComplexSignal
Y
=
fft
.
transform
(
y
);
ComplexSignal
H
=
fft
.
transform
(
h
);
ComplexSignal
H2
=
Operations
.
multiply
(
H
,
H
);
ComplexSignal
I
=
ComplexSignalFactory
.
identity
(
Y
.
nx
,
Y
.
ny
,
Y
.
nz
);
I
.
times
((
float
)
lambda
);
ComplexSignal
FA
=
Operations
.
add
(
H2
,
I
);
ComplexSignal
FT
=
Operations
.
divideStabilized
(
H
,
FA
);
ComplexSignal
X
=
Operations
.
multiply
(
Y
,
FT
);
RealSignal
x
=
fft
.
inverse
(
X
);
SignalCollector
.
free
(
FT
);
SignalCollector
.
free
(
Y
);
SignalCollector
.
free
(
H
);
SignalCollector
.
free
(
FA
);
SignalCollector
.
free
(
I
);
SignalCollector
.
free
(
H2
);
SignalCollector
.
free
(
X
);
return
x
;
}
public
RealSignal
runOptimizedMemoryFootprint
()
{
ComplexSignal
Y
=
fft
.
transform
(
y
);
ComplexSignal
H
=
fft
.
transform
(
h
);
ComplexSignal
X
=
filter
(
Y
,
H
);
SignalCollector
.
free
(
H
);
SignalCollector
.
free
(
Y
);
RealSignal
x
=
fft
.
inverse
(
X
);
SignalCollector
.
free
(
X
);
return
x
;
}
private
ComplexSignal
filter
(
ComplexSignal
Y
,
ComplexSignal
H
)
{
int
nx
=
H
.
nx
;
int
ny
=
H
.
ny
;
int
nz
=
H
.
nz
;
int
nxy
=
nx
*
ny
*
2
;
float
ya
,
yb
,
ha
,
hb
,
fa
,
fb
,
mag
,
ta
,
tb
;
float
epsilon2
=
(
float
)(
Operations
.
epsilon
*
Operations
.
epsilon
);
ComplexSignal
result
=
new
ComplexSignal
(
"TRIF"
,
nx
,
ny
,
nz
);
float
l
=
(
float
)
lambda
;
for
(
int
k
=
0
;
k
<
nz
;
k
++)
for
(
int
i
=
0
;
i
<
nxy
;
i
+=
2
)
{
ha
=
H
.
data
[
k
][
i
];
hb
=
H
.
data
[
k
][
i
+
1
];
ya
=
Y
.
data
[
k
][
i
];
yb
=
Y
.
data
[
k
][
i
+
1
];
fa
=
ha
*
ha
-
hb
*
hb
+
l
;
fb
=
2f
*
ha
*
hb
;
mag
=
fa
*
fa
+
fb
*
fb
;
ta
=
(
ha
*
fa
+
hb
*
fb
)
/
(
mag
>=
epsilon2
?
mag
:
epsilon2
);
tb
=
(
hb
*
fa
-
ha
*
fb
)
/
(
mag
>=
epsilon2
?
mag
:
epsilon2
);
result
.
data
[
k
][
i
]
=
ya
*
ta
-
yb
*
tb
;
result
.
data
[
k
][
i
+
1
]
=
ya
*
tb
+
ta
*
yb
;
}
return
result
;
}
@Override
public
int
getComplexityNumberofFFT
()
{
return
3
;
}
@Override
public
String
getName
()
{
return
"Tikhonov Regularization Inverse Filter"
;
}
@Override
public
String
[]
getShortnames
()
{
return
new
String
[]
{
"TRIF"
,
"TR"
};
}
@Override
public
double
getMemoryFootprintRatio
()
{
return
8.0
;
}
@Override
public
boolean
isRegularized
()
{
return
true
;
}
@Override
public
boolean
isStepControllable
()
{
return
false
;
}
@Override
public
boolean
isIterative
()
{
return
false
;
}
@Override
public
boolean
isWaveletsBased
()
{
return
false
;
}
@Override
public
void
setParameters
(
double
[]
params
)
{
if
(
params
==
null
)
return
;
if
(
params
.
length
>
0
)
lambda
=
(
float
)
params
[
0
];
}
@Override
public
double
[]
getDefaultParameters
()
{
return
new
double
[]
{
0.1
};
}
@Override
public
double
[]
getParameters
()
{
return
new
double
[]
{
lambda
};
}
@Override
public
double
getRegularizationFactor
()
{
return
lambda
;
}
@Override
public
double
getStepFactor
()
{
return
0.0
;
}
}
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