Functions |
template<class T > |
void | gpufilter::rcfr (T *inout, const int &w, const int &h, const T &b0, const T &a1, const bool &ff=false) |
| Compute first-order recursive filtering on columns forward and reverse.
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template<class T > |
void | gpufilter::rrfr (T *inout, const int &w, const int &h, const T &b0, const T &a1, const bool &ff=false) |
| Compute first-order recursive filtering on rows forward and reverse.
|
template<class T > |
void | gpufilter::r (T *inout, const int &w, const int &h, const T &b0, const T &a1, const bool &ff=false, const int &extb=0, const initcond &ic=zero) |
| Compute first-order recursive filtering.
|
template<class T > |
void | gpufilter::rcfr (T *inout, const int &w, const int &h, const T &b0, const T &a1, const T &a2, const bool &ff=false) |
| Compute second-order recursive filtering on columns forward and reverse.
|
template<class T > |
void | gpufilter::rrfr (T *inout, const int &w, const int &h, const T &b0, const T &a1, const T &a2, const bool &ff=false) |
| Compute second-order recursive filtering on rows forward and reverse.
|
template<class T > |
void | gpufilter::r (T *inout, const int &w, const int &h, const T &b0, const T &a1, const T &a2, const bool &ff=false, const int &extb=0, const initcond &ic=zero) |
| Compute second-order recursive filtering.
|
template<class T >
void gpufilter::r |
( |
T * |
inout, |
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const int & |
w, |
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const int & |
h, |
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const T & |
b0, |
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const T & |
a1, |
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const bool & |
ff = false , |
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const int & |
extb = 0 , |
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const initcond & |
ic = zero |
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) |
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Compute first-order recursive filtering.
Given an input 2D image compute a first-order recursive filtering on its columns and rows with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and a feedback coefficient, i.e. a weight on the previous element. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
[in] | extb | Extension (in blocks) to consider outside image (default 0) |
[in] | ic | Initial condition (for outside access) (default zero) |
- Template Parameters:
-
- Examples:
- example_bspline.cc, example_gauss.cc, example_r2.cc, example_r3.cc, example_r4.cc, example_r5.cc, example_sat2.cc, and example_sat3.cc.
template<class T >
void gpufilter::r |
( |
T * |
inout, |
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const int & |
w, |
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const int & |
h, |
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const T & |
b0, |
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const T & |
a1, |
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const T & |
a2, |
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const bool & |
ff = false , |
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const int & |
extb = 0 , |
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const initcond & |
ic = zero |
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) |
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Compute second-order recursive filtering.
Given an input 2D image compute a second-order recursive filtering on its columns and rows with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and two feedback coefficients, i.e. weights on the previous two elements. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | a2 | Feedback second-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
[in] | extb | Extension (in blocks) to consider outside image (default 0) |
[in] | ic | Initial condition (for outside access) (default zero) |
- Template Parameters:
-
template<class T >
void gpufilter::rcfr |
( |
T * |
inout, |
|
|
const int & |
w, |
|
|
const int & |
h, |
|
|
const T & |
b0, |
|
|
const T & |
a1, |
|
|
const bool & |
ff = false |
|
) |
| |
Compute first-order recursive filtering on columns forward and reverse.
Given an input 2D image compute a first-order recursive filtering on its columns with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and a feedback coefficient, i.e. a weight on the previous element. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
- Template Parameters:
-
template<class T >
void gpufilter::rcfr |
( |
T * |
inout, |
|
|
const int & |
w, |
|
|
const int & |
h, |
|
|
const T & |
b0, |
|
|
const T & |
a1, |
|
|
const T & |
a2, |
|
|
const bool & |
ff = false |
|
) |
| |
Compute second-order recursive filtering on columns forward and reverse.
Given an input 2D image compute a second-order recursive filtering on its columns with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and two feedback coefficients, i.e. weights on the previous two elements. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | a2 | Feedback second-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
- Template Parameters:
-
template<class T >
void gpufilter::rrfr |
( |
T * |
inout, |
|
|
const int & |
w, |
|
|
const int & |
h, |
|
|
const T & |
b0, |
|
|
const T & |
a1, |
|
|
const bool & |
ff = false |
|
) |
| |
Compute first-order recursive filtering on rows forward and reverse.
Given an input 2D image compute a first-order recursive filtering on its rows with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and a feedback coefficient, i.e. a weight on the previous element. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
- Template Parameters:
-
- Examples:
- example_r1.cc.
template<class T >
void gpufilter::rrfr |
( |
T * |
inout, |
|
|
const int & |
w, |
|
|
const int & |
h, |
|
|
const T & |
b0, |
|
|
const T & |
a1, |
|
|
const T & |
a2, |
|
|
const bool & |
ff = false |
|
) |
| |
Compute second-order recursive filtering on rows forward and reverse.
Given an input 2D image compute a second-order recursive filtering on its rows with a causal-anticausal filter pair. The filter is computed using a feedforward coefficient, i.e. a weight on the current element, and two feedback coefficients, i.e. weights on the previous two elements. The initial condition can be zero, clamp, repeat or mirror. The computation is done sequentially in a naïve single-core CPU fashion.
- Parameters:
-
[in,out] | inout | The 2D image to compute recursive filtering |
[in] | w | Width of the input image |
[in] | h | Height of the input image |
[in] | b0 | Feedforward coefficient |
[in] | a1 | Feedback first-order coefficient |
[in] | a2 | Feedback second-order coefficient |
[in] | ff | Forward-only (ignore anticausal filter) flag |
- Template Parameters:
-