| CvXPhotoBm3dDenoising Method (InputArray, OutputArray, Single, Int32, Int32, Int32, Int32, Int32, Int32, Single, NormTypes, Bm3dSteps, TransformTypes) |
Performs image denoising using the Block-Matching and 3D-filtering algorithm
(http://www.cs.tut.fi/~foi/GCF-BM3D/BM3D_TIP_2007.pdf) with several computational optimizations.Noise expected to be a gaussian white noise.
Namespace:
OpenCvSharp.XPhoto
Assembly:
OpenCvSharp (in OpenCvSharp.dll) Version: 1.0.0
Syntax public static void Bm3dDenoising(
InputArray src,
OutputArray dst,
float h = 1f,
int templateWindowSize = 4,
int searchWindowSize = 16,
int blockMatchingStep1 = 2500,
int blockMatchingStep2 = 400,
int groupSize = 8,
int slidingStep = 1,
float beta = 2f,
NormTypes normType = NormTypes.L2,
Bm3dSteps step = Bm3dSteps.STEPALL,
TransformTypes transformType = TransformTypes.HAAR
)
Public Shared Sub Bm3dDenoising (
src As InputArray,
dst As OutputArray,
Optional h As Single = 1F,
Optional templateWindowSize As Integer = 4,
Optional searchWindowSize As Integer = 16,
Optional blockMatchingStep1 As Integer = 2500,
Optional blockMatchingStep2 As Integer = 400,
Optional groupSize As Integer = 8,
Optional slidingStep As Integer = 1,
Optional beta As Single = 2F,
Optional normType As NormTypes = NormTypes.L2,
Optional step As Bm3dSteps = Bm3dSteps.STEPALL,
Optional transformType As TransformTypes = TransformTypes.HAAR
)
public:
static void Bm3dDenoising(
InputArray^ src,
OutputArray^ dst,
float h = 1f,
int templateWindowSize = 4,
int searchWindowSize = 16,
int blockMatchingStep1 = 2500,
int blockMatchingStep2 = 400,
int groupSize = 8,
int slidingStep = 1,
float beta = 2f,
NormTypes normType = NormTypes::L2,
Bm3dSteps step = Bm3dSteps::STEPALL,
TransformTypes transformType = TransformTypes::HAAR
)
static member Bm3dDenoising :
src : InputArray *
dst : OutputArray *
?h : float32 *
?templateWindowSize : int *
?searchWindowSize : int *
?blockMatchingStep1 : int *
?blockMatchingStep2 : int *
?groupSize : int *
?slidingStep : int *
?beta : float32 *
?normType : NormTypes *
?step : Bm3dSteps *
?transformType : TransformTypes
(* Defaults:
let _h = defaultArg h 1f
let _templateWindowSize = defaultArg templateWindowSize 4
let _searchWindowSize = defaultArg searchWindowSize 16
let _blockMatchingStep1 = defaultArg blockMatchingStep1 2500
let _blockMatchingStep2 = defaultArg blockMatchingStep2 400
let _groupSize = defaultArg groupSize 8
let _slidingStep = defaultArg slidingStep 1
let _beta = defaultArg beta 2f
let _normType = defaultArg normType NormTypes.L2
let _step = defaultArg step Bm3dSteps.STEPALL
let _transformType = defaultArg transformType TransformTypes.HAAR
*)
-> unit
Parameters
- src
- Type: OpenCvSharpInputArray
Input 8-bit or 16-bit 1-channel image. - dst
- Type: OpenCvSharpOutputArray
Output image with the same size and type as src. - h (Optional)
- Type: SystemSingle
Parameter regulating filter strength. Big h value perfectly removes noise but also
removes image details, smaller h value preserves details but also preserves some noise. - templateWindowSize (Optional)
- Type: SystemInt32
Size in pixels of the template patch that is used for block-matching. Should be power of 2. - searchWindowSize (Optional)
- Type: SystemInt32
Size in pixels of the window that is used to perform block-matching.
Affect performance linearly: greater searchWindowsSize - greater denoising time. Must be larger than templateWindowSize. - blockMatchingStep1 (Optional)
- Type: SystemInt32
Block matching threshold for the first step of BM3D (hard thresholding),
i.e.maximum distance for which two blocks are considered similar.Value expressed in euclidean distance. - blockMatchingStep2 (Optional)
- Type: SystemInt32
Block matching threshold for the second step of BM3D (Wiener filtering),
i.e.maximum distance for which two blocks are considered similar. Value expressed in euclidean distance. - groupSize (Optional)
- Type: SystemInt32
Maximum size of the 3D group for collaborative filtering. - slidingStep (Optional)
- Type: SystemInt32
Sliding step to process every next reference block. - beta (Optional)
- Type: SystemSingle
Kaiser window parameter that affects the sidelobe attenuation of the transform of the
window.Kaiser window is used in order to reduce border effects.To prevent usage of the window, set beta to zero. - normType (Optional)
- Type: OpenCvSharpNormTypes
Norm used to calculate distance between blocks. L2 is slower than L1 but yields more accurate results. - step (Optional)
- Type: OpenCvSharp.XPhotoBm3dSteps
Step of BM3D to be executed. Allowed are only BM3D_STEP1 and BM3D_STEPALL.
BM3D_STEP2 is not allowed as it requires basic estimate to be present. - transformType (Optional)
- Type: OpenCvSharp.XPhotoTransformTypes
Type of the orthogonal transform used in collaborative filtering step.
Currently only Haar transform is supported.
See Also