1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
|
/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkTikhonovDeconvolutionImageFilter_h
#define itkTikhonovDeconvolutionImageFilter_h
#include "itkInverseDeconvolutionImageFilter.h"
namespace itk
{
/**
* \class TikhonovDeconvolutionImageFilter
* \brief An inverse deconvolution filter regularized in the Tikhonov sense.
*
* The Tikhonov deconvolution filter is the inverse deconvolution
* filter with a regularization term added to the denominator.
* The filter minimizes the equation
* \f[ ||\hat{f} \otimes h - g||_{L_2}^2 + \mu||\hat{f}||^2
* \f]
* where \f$\hat{f}\f$ is the estimate of the unblurred image,
* \f$h\f$ is the blurring kernel, \f$g\f$ is the blurred image, and
* \f$\mu\f$ is a non-negative real regularization function.
*
* The filter applies a kernel described in the Fourier domain as
* \f$H^*(\omega) / (|H(\omega)|^2 + \mu)\f$ where \f$H(\omega)\f$ is
* the Fourier transform of \f$h\f$. The term \f$\mu\f$ is called
* RegularizationConstant in this filter. If \f$\mu\f$ is set to zero,
* this filter is equivalent to the InverseDeconvolutionImageFilter.
*
* \author Gaetan Lehmann, Biologie du Developpement et de la Reproduction, INRA de Jouy-en-Josas, France
* \author Cory Quammen, The University of North Carolina at Chapel Hill
*
* \ingroup ITKDeconvolution
*
*/
template <typename TInputImage,
typename TKernelImage = TInputImage,
typename TOutputImage = TInputImage,
typename TInternalPrecision = double>
class ITK_TEMPLATE_EXPORT TikhonovDeconvolutionImageFilter
: public InverseDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(TikhonovDeconvolutionImageFilter);
using Self = TikhonovDeconvolutionImageFilter;
using Superclass = InverseDeconvolutionImageFilter<TInputImage, TKernelImage, TOutputImage, TInternalPrecision>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(TikhonovDeconvolutionImageFilter);
/** Dimensionality of input and output data is assumed to be the same. */
static constexpr unsigned int ImageDimension = TInputImage::ImageDimension;
using InputImageType = TInputImage;
using OutputImageType = TOutputImage;
using KernelImageType = TKernelImage;
using typename Superclass::InputPixelType;
using typename Superclass::OutputPixelType;
using typename Superclass::KernelPixelType;
using typename Superclass::InputIndexType;
using typename Superclass::OutputIndexType;
using typename Superclass::KernelIndexType;
using typename Superclass::InputSizeType;
using typename Superclass::OutputSizeType;
using typename Superclass::KernelSizeType;
using typename Superclass::SizeValueType;
using typename Superclass::InputRegionType;
using typename Superclass::OutputRegionType;
using typename Superclass::KernelRegionType;
/** Internal image types. */
using typename Superclass::InternalImageType;
using typename Superclass::InternalImagePointerType;
using typename Superclass::InternalComplexType;
using typename Superclass::InternalComplexImageType;
using typename Superclass::InternalComplexImagePointerType;
/** The regularization factor. Larger values reduce the dominance of
* noise in the solution, but results in higher approximation error
* in the deblurred image. Default value is 0.0, yielding the same
* results as the InverseDeconvolutionImageFilter. */
itkSetMacro(RegularizationConstant, double);
itkGetConstMacro(RegularizationConstant, double);
protected:
TikhonovDeconvolutionImageFilter();
~TikhonovDeconvolutionImageFilter() override = default;
/** This filter uses a minipipeline to compute the output. */
void
GenerateData() override;
void
PrintSelf(std::ostream & os, Indent indent) const override;
private:
double m_RegularizationConstant{};
};
namespace Functor
{
template <typename TInput1, typename TInput2, typename TOutput>
class ITK_TEMPLATE_EXPORT TikhonovDeconvolutionFunctor
{
public:
TikhonovDeconvolutionFunctor() = default;
~TikhonovDeconvolutionFunctor() = default;
TikhonovDeconvolutionFunctor(const TikhonovDeconvolutionFunctor & f)
: m_RegularizationConstant(f.m_RegularizationConstant)
, m_KernelZeroMagnitudeThreshold(f.m_KernelZeroMagnitudeThreshold)
{}
bool
operator==(const TikhonovDeconvolutionFunctor &) const
{
return true;
}
ITK_UNEQUAL_OPERATOR_MEMBER_FUNCTION(TikhonovDeconvolutionFunctor);
inline TOutput
operator()(const TInput1 & I, const TInput2 & H) const
{
typename TOutput::value_type normH = std::norm(H);
typename TOutput::value_type denominator = normH + m_RegularizationConstant;
TOutput value{};
if (denominator >= m_KernelZeroMagnitudeThreshold)
{
value = static_cast<TOutput>(I * (std::conj(H) / denominator));
}
return value;
}
/** Set/get the regular constant. This needs to be a non-negative
* real value. */
void
SetRegularizationConstant(double constant)
{
m_RegularizationConstant = constant;
}
double
GetRegularizationConstant() const
{
return m_RegularizationConstant;
}
/** Set/get the threshold value below which complex magnitudes are considered
* to be zero. */
void
SetKernelZeroMagnitudeThreshold(double mu)
{
m_KernelZeroMagnitudeThreshold = mu;
}
double
GetKernelZeroMagnitudeThreshold() const
{
return m_KernelZeroMagnitudeThreshold;
}
private:
double m_RegularizationConstant = 0.0;
double m_KernelZeroMagnitudeThreshold = 0.0;
};
} // namespace Functor
} // namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkTikhonovDeconvolutionImageFilter.hxx"
#endif
#endif
|
