ImageDev

- Getting Started
- ImageDev Python
- ImageDev C#
- ImageDev C++
- Licensing Information
- Redistributable Files
- Processing
- Image Manipulation
- - Synthetic Images
  - - RandomImage2d
    - RandomImage3d
    - RandomGaussianImage2d
    - RandomGaussianImage3d
    - RadialFunctionImage2d
    - RadialFunctionImage3d
    - OrientedLinesImage2d
    - OrientedLinesImage3d
    - RandomSphereImage3d
    - BoxGridImage2d
    - BoxGridImage3d
    - WedgeMaskImage3d
    - PointSpreadFunctionImage3d
  - Image Editing
  - - ConvertImage
    - ResetImage
    - CopyImage
    - PasteImage2d
    - PasteImage3d
    - CropImage2d
    - CropImage3d
    - SampleImage2d
    - SampleImage3d
    - InterlaceFrames2d
    - DeinterlaceFrames2d
  - Stack Operations
  - - SequenceConcatenation
    - SequenceToMultispectral
    - SequenceToVolume
    - VolumeToSequence
    - MultispectralToSequence
    - GetSliceFromVolume3d
    - SetSliceToVolume3d
    - GetObliqueSliceFromVolume3d
    - ImageStackProjection3d
    - VolumeProjection3d
- Geometry And Matching
- - Geometric Transforms
  - - Shearing3d
    - TranslateImage2d
    - TranslateImage3d
    - Flip2d
    - Flip3d
    - FlipImage2d
    - FlipImage3d
    - TransposeImage2d
    - TransposeImage3d
    - SwapQuadrants
    - Rotate2d
    - Rotate3d
    - RotationGenerator3d
    - Resize2d
    - Resize3d
    - RescaleByFactor
    - RescaleImageByFactor
    - ResampleAffine2d
    - ResampleAffine3d
    - ResampleElastic2d
    - SurfaceUnfolding3d
  - Pattern Recognition
  - - BinaryCorrelation2d
    - CrossCorrelation2d
    - CorrelationByDifference2d
    - CorrelationBySignChange2d
  - Registration
  - - ImagePrealignment3d
    - AffineRegistration
    - ElasticRegistration2d
- Arithmetic And Logic
- - Arithmetical Operations
  - - AbsoluteValue
    - AbsoluteMaximumWithImage
    - ArithmeticOperationWithImage
    - ArithmeticOperationWithValue
    - BlendWithImage
    - BlendWithValue
    - DirectionalBlending2d
    - CombineByMask
    - ImageFormula
    - EigenDecomposition2d
    - EigenDecomposition3d
  - Logical Operations
  - - MaskImage
    - LogicalOperationWithImage
    - LogicalOperationWithValue
    - LogicalNot
    - InvertImage
    - BitShift
- Image Filtering
- - Grayscale Transforms
  - - RescaleIntensity
    - StackRescaleIntensity
    - MatchContrast
    - HistogramEqualization
    - Clahe2d
    - Clahe3d
    - AdaptiveHistogramEqualization
    - ApplyGrayscaleLut
    - BackgroundImage2d
    - RadialBackgroundImage2d
    - ShadingCorrection
    - Exponential
    - Logarithm
    - Square
    - SquareRoot
    - RingArtifactRemoval3d
    - BeamHardeningCorrection3d
  - Color Transforms
  - - ColorToLightness
    - GetColorChannel
    - ColorToGrayscale
    - GrayscaleToColor
    - ColorSpaceConversion
    - ColorAntialiasing2d
    - BayerToRgb2d
    - DecorrelationStretch2d
    - BlendAlphaChannelWithImage
    - BlendAlphaChannelWithValue
  - Smoothing And Denoising
  - - BoxFilter2d
    - BoxFilter3d
    - MedianFilter2d
    - MedianFilter3d
    - GaussianFilter2d
    - GaussianFilter3d
    - ExponentialFilter2d
    - RecursiveExponentialFilter2d
    - RecursiveExponentialFilter3d
    - NonLocalMeansFilter2d
    - NonLocalMeansFilter3d
    - CurvatureDrivenDiffusion
    - BilateralFilter2d
    - BilateralFilter3d
    - MajorityFilter2d
    - MajorityFilter3d
    - SigmaFilter2d
    - SigmaFilter3d
    - Despeckle2d
    - Despeckle3d
    - NagaoFilter2d
    - NagaoFilter3d
    - SnnFilter2d
    - SnnFilter3d
    - FlowInpainting
  - Sharpening
  - - Deblurring2d
    - Delineate2d
    - Delineate3d
    - UnsharpMasking2d
    - UnsharpMasking3d
    - Shade2d
    - RichardsonLucyDeconvolution
  - Texture Filters
  - - MeanFilter2d
    - MeanFilter3d
    - VarianceFilter2d
    - VarianceFilter3d
    - SkewnessFilter2d
    - SkewnessFilter3d
    - KurtosisFilter2d
    - KurtosisFilter3d
    - ContrastFilter2d
    - ContrastFilter3d
    - VariationFilter2d
    - VariationFilter3d
    - EnergyFilter2d
    - EnergyFilter3d
    - EntropyFilter2d
    - EntropyFilter3d
    - AutocorrelationFilter
    - LocalAutocorrelationFilter2d
    - ImageCurvature2d
    - ImageCurvature3d
    - StructureEnhancementFilter2d
    - StructureEnhancementFilter3d
    - ScaleSpaceLogFilter2d
    - ScaleSpaceLogFilter3d
  - Frequency Domain
  - - ComplexFft
    - ComplexFftInverse
    - ComplexCenteredFft
    - ComplexCenteredFftInverse
    - CartesianToPolar2d
    - PolarToCartesian2d
    - ConvolutionWithImage
    - ConvolutionWithImage2d
    - RadialFrequencyFilter2d
    - GaborFiltering2d
- Edge Detection
- - Gradient
  - - GradientOperator2d
    - GradientOperator3d
    - VectorToMagnitude2d
    - VectorToMagnitude3d
    - GradientVector2d
    - GradientVector3d
    - GradientMagnitude2d
    - GradientMagnitude3d
    - MorphologicalGradient2d
    - MorphologicalGradient3d
    - RadialGradientCentered2d
    - RadialGradientCentered3d
    - RadialGradientContour2d
    - RadialGradientContour3d
    - RadialGradientLabel2d
    - RadialGradientLabel3d
  - Laplacian
  - - DobFilter2d
    - DobFilter3d
    - MorphologicalLaplacian
    - RecursiveLaplacian2d
    - RecursiveLaplacian3d
  - Other Derivatives
  - - GaussianDerivative2d
    - GaussianDerivative3d
    - GaussianHessianMatrix2d
    - GaussianHessianMatrix3d
    - GaussianGradientTensor2d
    - GaussianGradientTensor3d
  - Edge Marking
  - - ImageLocalMaxima2d
    - ImageLocalMaxima3d
    - RidgeDetection
    - ZeroCrossings2d
    - DijkstraShortestPath2d
    - EigenvaluesToStructureness2d
    - EigenvaluesToStructureness3d
    - TensorVoting2d
    - CannyEdgeDetector2d
    - CannyEdgeDetector3d
- Image Segmentation
- - Binarization
  - - Thresholding
    - ThresholdingByCriterion
    - AutoThresholdingBright
    - AutoThresholdingDark
    - ColorThresholding
    - TopHat
    - FeatureAdaptiveThresholding
    - AdaptiveThresholding2d
    - AdaptiveThresholding3d
    - HysteresisThresholding
  - Labeling
  - - Labeling2d
    - Labeling3d
    - ReorderLabels
    - AssignLabel
    - AddObjectToLabel
    - GroupCloseLabelsl2d
  - Feature Selection
  - - KillBorder2d
    - KillBorder3d
    - RemoveSmallSpots
    - ObjectBoundaries
    - BinarySmoothing2d
    - BinarySmoothing3d
    - LabelInterfaces
  - Separating And Filling
  - - FillHoles2d
    - FillHoles3d
    - RemoveSmallHoles
    - FillImageBorder3d
    - ExpandLabelBoundaries
    - SeparateObjects
    - HExtremaWatershed
    - SeparateObjectsByBlock
    - HExtremaWatershedByBlock
    - ConvexHull2d
  - Region Growing
  - - MarkerBasedWatershed2d
    - MarkerBasedWatershed3d
    - MarkerBasedWatershedByBlock
    - MarkerBasedWatershedBasinsByBlock
    - ExpandLabelsOnBackground
    - FloodFillThreshold2d
    - FloodFillThreshold3d
    - Waterpixel
  - Computational Geometry
  - - PolylineResampler2d
    - PolylineResampler3d
    - PolylineExtrusion3d
    - SurfaceFitting3d
- Mathematical Morphology
- - Erosion And Dilation
  - - Erosion2d
    - Erosion3d
    - Dilation2d
    - Dilation3d
    - ErosionDisk2d
    - ErosionDisk3d
    - ErosionBall3d
    - DilationDisk2d
    - DilationDisk3d
    - DilationBall3d
    - ErosionLine2d
    - ErosionLine3d
    - DilationLine2d
    - DilationLine3d
    - ErosionColor2d
    - DilationColor2d
    - SelectiveErosion2d
    - SelectiveErosion3d
    - SelectiveDilation2d
    - SelectiveDilation3d
  - Opening And Closing
  - - Opening2d
    - Opening3d
    - Closing2d
    - Closing3d
    - OpeningDisk2d
    - OpeningDisk3d
    - OpeningBall3d
    - ClosingDisk2d
    - ClosingDisk3d
    - ClosingBall3d
    - OpeningLine2d
    - OpeningLine3d
    - ClosingLine2d
    - ClosingLine3d
    - OpeningLineMaximum
    - ClosingLineMinimum
    - OpeningColor2d
    - ClosingColor2d
    - OpeningByReconstruction2d
    - OpeningByReconstruction3d
    - ClosingByReconstruction2d
    - ClosingByReconstruction3d
    - OpeningDiskByReconstruction2d
    - OpeningBallByReconstruction3d
    - ClosingDiskByReconstruction2d
    - ClosingBallByReconstruction3d
    - SelectiveOpening2d
    - SelectiveOpening3d
    - SelectiveClosing2d
    - SelectiveClosing3d
  - Hit Or Miss And Skeleton
  - - Skeleton
    - Centerline3d
    - InfluenceZones
    - Centroid2d
    - EndPoints2d
    - EndPoints3d
    - Pruning2d
    - Pruning3d
    - IsolatedPoints2d
    - IsolatedPoints3d
    - TriplePoints2d
    - InteriorPoints2d
    - ApplyMorphologicalLut2d
    - ApplyMorphologicalLut3d
  - Geodesic Transformations
  - - ReconstructionFromMarkers2d
    - ReconstructionFromMarkers3d
    - GrayscaleReconstruction2d
    - GrayscaleReconstruction3d
    - GrayscaleFillHoles2d
    - GrayscaleFillHoles3d
    - HExtrema2d
    - HExtrema3d
    - RegionalExtrema2d
    - RegionalExtrema3d
    - UltimateErosion
  - Distance Maps
  - - DistanceMap2d
    - DistanceMap3d
    - EuclideanDistanceMap3d
    - EuclideanDistanceMap2d
    - GeodesicDistanceMap
    - ClosestBoundaryPoints2d
    - ClosestBoundaryPoints3d
    - TimeMap
    - LocalThicknessMap3d
    - GeodesicPropagation2d
    - GeodesicMaxima
    - GeodesicMinima
- Image Analysis
- - Image Measurements
  - - BinaryAnalysis
    - Area2d
    - Area3d
    - IntensityIntegral2d
    - IntensityIntegral3d
    - ObjectCount
    - DistanceMean2d
    - EulerNumber2d
    - EulerNumber3d
    - FractalDimension
    - CurvatureIntegrals3d
  - Image Statistics
  - - IntensityExtrema
    - IntensityStatistics
    - IntensityMaskedStatistics
    - IntensityHistogram
    - IntensityBinHistogram
    - AutoThresholdingValue
    - CompareImage
    - CompareValue
    - SimilarityMetricValue
    - SsimValue2d
    - SsimValue3d
    - RegionSimilarity
    - RadialIntensityProfile2d
    - RadialIntensityProfile3d
    - CylindricalIntensityProfile3d
    - RadialAutocorrelationProfile
    - Cooccurrence2d
    - MeasureGaussianNoise
    - LocalMaxima2d
    - LocalMaxima3d
    - PeakExtractor
    - LocalDensityMap2d
    - LocalDensityMap3d
    - SegmentationMetrics
  - Individual Measurements
  - - VolumeFractionByLabel
    - LabelCountByRegion
    - VolumeFractionByRegion
    - LabelAnalysis
    - LabelFiltering
    - LabelFilteringAnalysis
    - FilterAnalysis
    - MeasurementToImage
    - FilterByMeasurement
    - SieveLabeling
  - Moments And Orientations
  - - MomentsOfInertia2d
    - MomentsOfInertia3d
    - InertiaMoment2d
    - Eccentricity2d
    - OrientationMapFourier3d
    - OrientationMatchingFourier3d
  - Morphometry
  - - ObjectVolume
    - TotalImageVolume
    - ObjectBoundaryArea
    - TotalImageBoundaryArea
    - AverageObjectAreaPerSlice3d
    - AverageObjectCountPerSlice3d
    - ObjectImageRatio
    - ObjectSpecificSurface
    - ObjectSurfaceDensity
    - PorosityPercentage3d
    - FragmentationIndex
    - StructureModelIndex3d
    - AverageObjectThickness3d
    - AverageSpaceThickness3d
    - ObjectLinearDensity3d
    - DegreeOfAnisotropy
  - Propagation
  - - AxisPropagationBinary3d
    - AxisPropagationBinaryWithSeeds3d
    - AxisPropagationLabel3d
    - AxisPropagationLabelWithSeeds3d
    - AxisPropagationDistance3d
    - EstimateShortestPath3d
    - PercolationThreshold3d
    - AxisConnectivity3d
    - CentroidPathTortuosity3d
- Machine Learning
- - Classification
  - - AutoSegmentation3Phases
    - AutoIntensityClassification
    - SupervisedTextureClassification2d
    - SupervisedTextureClassification3d
    - TextureClassificationCreate
    - TextureClassificationTrain
    - TextureClassificationApply
  - Deep learning
  - - OnnxPredictionFiltering2d
    - OnnxPredictionFiltering3d
    - OnnxPredictionSegmentation2d
    - OnnxPredictionSegmentation3d
    - OnnxPredictionTwoOutputs2d
    - OnnxPredictionTwoOutputs3d
- Business Application
- - Dental
  - - DentalWallSegmentation3d
    - DentalMeshExtraction3d
    - DentalUnfolding3d
- IO
- - Image Data
  - - ReadVipImage
    - WriteVipImage
  - Algebraic Data
  - - ReadArrayXd
    - ReadArrayXi64
    - WriteArrayXd
    - WriteArrayXi64
  - Model
  - - ReadOnnxModel
- CUDA
- - Geometry And Matching
  - - CudaCrossCorrelation2d
    - CudaTemplateMatching3d
  - Image Filtering
  - - CudaBoxFilter2d
    - CudaMedianFilter2d
    - CudaGaussianFilter3d
    - CudaAdaptiveManifoldNlmFilter
  - Mathematical Morphology
  - - CudaErosion2d
    - CudaDilation2d
    - CudaOpening2d
    - CudaClosing2d
- Utilities
- Initialization
- Processing
- Syntax Check
- Visualization
- Data Object
- Image Data
- Kernels
- - MorphologicalKernel2d
  - MorphologicalKernel3d
  - MorphologicalLut2d
  - MorphologicalLut3d
- Model
- - TextureClassificationModel
  - OnnxModel
- Global Measurements
- - Image Measurements
  - - Area2dMsr
    - Area3dMsr
    - IntensityIntegral2dMsr
    - IntensityIntegral3dMsr
    - ObjectCountMsr
    - DistanceMeanMsr
    - Euler2dMsr
    - Euler3dMsr
    - FractalMsr
    - CurvatureIntegralsMsr
  - Image Statistics
  - - IntensityExtremaMsr
    - StatisticsMsr
    - IntensityHistogramMsr
    - IntensityBinHistogramMsr
    - CompareMsr
    - SimilarityMetricValueMsr
    - SsimValueMsr
    - RegionSimilarityMsr
    - SegmentationMetricsObjectMsr
    - SegmentationMetricsImageMsr
    - CylindricalIntensityProfileMsr
    - RadialIntensityProfileMsr
    - CorrelationMsr
    - CoocurrrenceMsr
    - GaussianNoiseMsr
    - LocalMaximaMsr
    - PeakExtractorMsr
  - Individual Measurements
  - - VolumeFractionByLabelMsr
    - LabelCountByRegionMsr
    - VolumeFractionByRegionMsr
  - Moments And Orientations
  - - MomentsOfInertia2dMsr
    - MomentsOfInertia3dMsr
    - MeanInertiaMomentMsr
    - Eccentricity2dMsr
    - LocalOrientation3dMsr
  - Morphometry
  - - ObjectVolumeMsr
    - ImageVolumeMsr
    - ObjectBoundaryAreaMsr
    - ImageBoundaryAreaMsr
    - AverageObjectAreaPerSliceMsr
    - AverageObjectCountPerSliceMsr
    - ObjectImageRatioMsr
    - ObjectSpecificSurfaceMsr
    - ObjectSurfaceDensityMsr
    - PorosityPercentageMsr
    - FragmentationMsr
    - StructureModelIndexMsr
    - AverageObjectThicknessMsr
    - AverageSpaceThicknessMsr
    - ObjectLinearDensityMsr
    - AnisotropyDegreeMsr
  - Propagation
  - - AxisPropagationBinary3dMsr
    - AxisPropagationLabel3dMsr
    - AxisPropagationDistance3dMsr
    - PercolationThreshold3dMsr
    - CentroidTortuosity3dMsr
  - Geometry And Matching
  - - CrossCorrelation2dMsr
  - Edge Detection
  - - CannyEdgeDetectorMsr
  - Image Segmentation
  - - AutoThresholdingMsr
    - AutoSegmentation3PhasesMsr
  - Mathematical Morphology
  - - ApplyMorphologicalLutMsr
- Analysis Measurements
- - AnalysisMsr
  - Measurement
  - Native Measurements
  - - Geometry
    - Feret
    - Intensity
    - Histogram
    - Inertia
    - Locate
    - Segment
    - Cooccurrence
  - CustomMeasurement
  - MeasurementAttributes
  - - Quantile
    - Histogram
    - Feret2d
    - Feret3d
    - NeighborCount
    - Cooccurrence
    - Breadth3d
  - MeasurementGroup
- Measurement Information
- - FieldInformation
  - CustomMeasurementInfo
- CUDA
- - CudaContext
- User Guide
- First Steps
- - Hello ImageDev
  - Initialize ImageDev
- Image Management
- - Create 2D Image
  - Create 3D Image
  - Data Transfer
- Standard Processing
- - Denoising
  - Thresholding
- Image Analysis
- - Global Analysis
  - Label Analysis
  - Measurement Browsing
  - Measurement Group
  - Custom Analysis
- Advanced Processing
- - Algorithm Tracking
- Typical Use Cases
- - Particle Analysis
  - Pore Analysis

# ShadingCorrection

Corrects the lighting conditions of an image according to a black and a white reference.
Access to parameter description
This algorithm is bound for correcting the shading, that is to say the lighting standardisation. It is an histogram spreading performed pixelwise.
The process needs a white and a black reference for each pixel to apply the right correction.
The output image $O$ is computed from the input $I$ by applying the formula:
$$ O(i,j)=\left(\frac{I(i,j)-B(i,j)}{W(i,j)-B(i,j)}\right)\times F $$ Where:

- $B$ is the black reference image,
- $W$ is the white reference image,
- $F$ is a normalization factor. If each pixel of the input image is lower than its counterpart in the white reference, it can be its minimum intensity. Else, it must be lower than the maximum to avoid overflows. A value of 200 usually works well with unsigned 8-bit images.

There are several ways to get a white reference:

- At the acquisition, by grabbing an image of a void field (white paper in photography, slice without sample in light microscopy by transmission, defocused bright sample in light microscopy by reflexion).
- With image processing; for example, by applying a Morphological Closing or a Background estimation.

The black reference is generally omitted, which amounts to using the input image minimum intensity as a constant black reference. Otherwise, it can be generated in an analogous way to the white reference.
See also

- BackgroundImage2d
- RescaleIntensity
- MatchContrast

##
Function Syntax

This function returns outputImage.

    // Function prototype
    std::shared_ptr< iolink::ImageView >
    shadingCorrection( std::shared_ptr< iolink::ImageView > inputImage,
                       std::shared_ptr< iolink::ImageView > inputBlackReferenceImage,
                       std::shared_ptr< iolink::ImageView > inputWhiteReferenceImage,
                       double normalizationFactor,
                       std::shared_ptr< iolink::ImageView > outputImage = nullptr );

This function returns outputImage.

    // Function prototype.
    shading_correction(input_image: idt.ImageType,
                       input_black_reference_image: idt.ImageType,
                       input_white_reference_image: idt.ImageType,
                       normalization_factor: float = 200,
                       output_image: idt.ImageType = None) -> idt.ImageType

This function returns outputImage.

    // Function prototype.
    public static IOLink.ImageView
    ShadingCorrection( IOLink.ImageView inputImage,
                       IOLink.ImageView inputBlackReferenceImage,
                       IOLink.ImageView inputWhiteReferenceImage,
                       double normalizationFactor = 200,
                       IOLink.ImageView outputImage = null );

##
Class Syntax

    // Command constructor.
    ShadingCorrection();

    /// Gets the inputImage parameter.
    /// The input image to correct.
    std::shared_ptr< iolink::ImageView > inputImage() const;
    /// Sets the inputImage parameter.
    /// The input image to correct.
    void setInputImage( std::shared_ptr< iolink::ImageView > inputImage );

    /// Gets the inputBlackReferenceImage parameter.
    /// The input black reference image. If it equals null, the input image minimum is used as a constant value. This image must have same dimensions and type as the input image.
    std::shared_ptr< iolink::ImageView > inputBlackReferenceImage() const;
    /// Sets the inputBlackReferenceImage parameter.
    /// The input black reference image. If it equals null, the input image minimum is used as a constant value. This image must have same dimensions and type as the input image.
    void setInputBlackReferenceImage( std::shared_ptr< iolink::ImageView > inputBlackReferenceImage );

    /// Gets the inputWhiteReferenceImage parameter.
    /// The input white reference image. If it equals null, the input image maximum is used as a constant value. This image must have same dimensions and type as the input image.
    std::shared_ptr< iolink::ImageView > inputWhiteReferenceImage() const;
    /// Sets the inputWhiteReferenceImage parameter.
    /// The input white reference image. If it equals null, the input image maximum is used as a constant value. This image must have same dimensions and type as the input image.
    void setInputWhiteReferenceImage( std::shared_ptr< iolink::ImageView > inputWhiteReferenceImage );

    /// Gets the normalizationFactor parameter.
    /// The normalization factor.
    double normalizationFactor() const;
    /// Sets the normalizationFactor parameter.
    /// The normalization factor.
    void setNormalizationFactor( const double& normalizationFactor );

    /// Gets the outputImage parameter.
    /// The output image. Its dimensions and type are forced to the same values as the input.
    std::shared_ptr< iolink::ImageView > outputImage() const;
    /// Sets the outputImage parameter.
    /// The output image. Its dimensions and type are forced to the same values as the input.
    void setOutputImage( std::shared_ptr< iolink::ImageView > outputImage );

    // Method to launch the command.
    void execute();

    # Property of the inputImage parameter.
    ShadingCorrection.input_image
    # Property of the inputBlackReferenceImage parameter.
    ShadingCorrection.input_black_reference_image
    # Property of the inputWhiteReferenceImage parameter.
    ShadingCorrection.input_white_reference_image
    # Property of the normalizationFactor parameter.
    ShadingCorrection.normalization_factor
    # Property of the outputImage parameter.
    ShadingCorrection.output_image

    // Method to launch the command.
    execute()

    // Command constructor.
    ShadingCorrection()

    // Property of the inputImage parameter.
    ShadingCorrection.inputImage
    // Property of the inputBlackReferenceImage parameter.
    ShadingCorrection.inputBlackReferenceImage
    // Property of the inputWhiteReferenceImage parameter.
    ShadingCorrection.inputWhiteReferenceImage
    // Property of the normalizationFactor parameter.
    ShadingCorrection.normalizationFactor
    // Property of the outputImage parameter.
    ShadingCorrection.outputImage

    // Method to launch the command.
    Execute()

## Parameters

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputImage | The input image to correct. | Image | Binary, Label, Grayscale or Multispectral | nullptr
| inputBlackReferenceImage | The input black reference image. If it equals null, the input image minimum is used as a constant value. This image must have same dimensions and type as the input image. | Image | Binary, Label, Grayscale or Multispectral | nullptr
| inputWhiteReferenceImage | The input white reference image. If it equals null, the input image maximum is used as a constant value. This image must have same dimensions and type as the input image. | Image | Binary, Label, Grayscale or Multispectral | nullptr
| normalizationFactor | The normalization factor. | Float64 | Any value | 200
| outputImage | The output image. Its dimensions and type are forced to the same values as the input. | Image |  | nullptr

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| input_image | The input image to correct. | image | Binary, Label, Grayscale or Multispectral | None
| input_black_reference_image | The input black reference image. If it equals null, the input image minimum is used as a constant value. This image must have same dimensions and type as the input image. | image | Binary, Label, Grayscale or Multispectral | None
| input_white_reference_image | The input white reference image. If it equals null, the input image maximum is used as a constant value. This image must have same dimensions and type as the input image. | image | Binary, Label, Grayscale or Multispectral | None
| normalization_factor | The normalization factor. | float64 | Any value | 200
| output_image | The output image. Its dimensions and type are forced to the same values as the input. | image |  | None

Parameter Name | Description | Type | Supported Values | Default Value
--- | --- | --- | --- | ---
| inputImage | The input image to correct. | Image | Binary, Label, Grayscale or Multispectral | null
| inputBlackReferenceImage | The input black reference image. If it equals null, the input image minimum is used as a constant value. This image must have same dimensions and type as the input image. | Image | Binary, Label, Grayscale or Multispectral | null
| inputWhiteReferenceImage | The input white reference image. If it equals null, the input image maximum is used as a constant value. This image must have same dimensions and type as the input image. | Image | Binary, Label, Grayscale or Multispectral | null
| normalizationFactor | The normalization factor. | Float64 | Any value | 200
| outputImage | The output image. Its dimensions and type are forced to the same values as the input. | Image |  | null

## Object Examples

    auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

    ShadingCorrection shadingCorrectionAlgo;
    shadingCorrectionAlgo.setInputImage( foam );
    shadingCorrectionAlgo.setInputBlackReferenceImage( foam );
    shadingCorrectionAlgo.setInputWhiteReferenceImage( foam );
    shadingCorrectionAlgo.setNormalizationFactor( 200.0 );
    shadingCorrectionAlgo.execute();

    std::cout << "outputImage:" << shadingCorrectionAlgo.outputImage()->toString();

    foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

    shading_correction_algo = imagedev.ShadingCorrection()
    shading_correction_algo.input_image = foam
    shading_correction_algo.input_black_reference_image = foam
    shading_correction_algo.input_white_reference_image = foam
    shading_correction_algo.normalization_factor = 200.0
    shading_correction_algo.execute()

    print("output_image:", str(shading_correction_algo.output_image))

    ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

    ShadingCorrection shadingCorrectionAlgo = new ShadingCorrection
    {
        inputImage = foam,
        inputBlackReferenceImage = foam,
        inputWhiteReferenceImage = foam,
        normalizationFactor = 200.0
    };
    shadingCorrectionAlgo.Execute();

    Console.WriteLine( "outputImage:" + shadingCorrectionAlgo.outputImage.ToString() );

## Function Examples

    auto foam = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "foam.vip" );

    auto result = shadingCorrection( foam, foam, foam, 200.0 );

    std::cout << "outputImage:" << result->toString();

    foam = imagedev.read_vip_image(imagedev_data.get_image_path("foam.vip"))

    result = imagedev.shading_correction(foam, foam, foam, 200.0)

    print("output_image:", str(result))

    ImageView foam = Data.ReadVipImage( @"Data/images/foam.vip" );

    IOLink.ImageView result = Processing.ShadingCorrection( foam, foam, foam, 200.0 );

    Console.WriteLine( "outputImage:" + result.ToString() );

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