MeasureGaussianNoise

Computes an estimation of a gaussian noise standard deviation in an image.

Access to parameter description

This algorithm filters the input image using a zero mean operator that is almost insensitive to image structures. The variance of the result is an estimate of the noise variance. The kernel for filtering the image is $$K = \begin{bmatrix} 1 & -2 & 1\\ -2 & 4 & -2\\ 1 & -2 & 1 \end{bmatrix} $$ which has a zero mean and variance of 36 times the image noise variance. In 2D, the variance of the noise $n$ contained in the image $I$ can then be estimated as $$ \sigma^2_n=\frac{1}{36(M-2)(N-2)}\sum^{M}_{i=1}\sum^{N}_{j=1}(I(i,j)\ast K)^2$$ where $M$ is the image height in pixels, $N$ the image width in pixels and $I(i,j)\ast K$ is the convolution of the image $I$ by the kernel $K$ at the position $(i,j)$.

Reference
J. Immerkoer, "Fast Noise Variance Estimation". Computer Vision and Image Understanding. vol. 64, no. 2, pp. 300-302, Sep. 1996.

See also

• IntensityStatistics
• GaussianFilter2d

// Function prototype
GaussianNoiseMsr::Ptr
measureGaussianNoise( std::shared_ptr< iolink::ImageView > inputImage, GaussianNoiseMsr::Ptr outputMeasurement = nullptr );

// Function prototype.
measure_gaussian_noise(input_image: idt.ImageType,
                       output_measurement: Union[Any, None] = None) -> GaussianNoiseMsr

// Function prototype.
public static GaussianNoiseMsr
MeasureGaussianNoise( IOLink.ImageView inputImage, GaussianNoiseMsr outputMeasurement = null );

// Command constructor.
MeasureGaussianNoise();

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

/// Gets the outputMeasurement parameter.
/// The output measurement result.
GaussianNoiseMsr::Ptr outputMeasurement() const;


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

# Property of the inputImage parameter.
MeasureGaussianNoise.input_image
# Property of the outputMeasurement parameter.
MeasureGaussianNoise.output_measurement

// Method to launch the command.
execute()

// Command constructor.
MeasureGaussianNoise()

// Property of the inputImage parameter.
MeasureGaussianNoise.inputImage
// Property of the outputMeasurement parameter.
MeasureGaussianNoise.outputMeasurement

// Method to launch the command.
Execute()

auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

MeasureGaussianNoise measureGaussianNoiseAlgo;
measureGaussianNoiseAlgo.setInputImage( polystyrene );
measureGaussianNoiseAlgo.execute();

std::cout << "standardDeviation: " << measureGaussianNoiseAlgo.outputMeasurement()->standardDeviation( 0 ) ;

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

measure_gaussian_noise_algo = imagedev.MeasureGaussianNoise()
measure_gaussian_noise_algo.input_image = polystyrene
measure_gaussian_noise_algo.execute()

print("standardDeviation: ", str(measure_gaussian_noise_algo.output_measurement.standard_deviation(0)))

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

MeasureGaussianNoise measureGaussianNoiseAlgo = new MeasureGaussianNoise
{
    inputImage = polystyrene
};
measureGaussianNoiseAlgo.Execute();

Console.WriteLine( "standardDeviation: " + measureGaussianNoiseAlgo.outputMeasurement.standardDeviation( 0 ) );

auto polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );

auto result = measureGaussianNoise( polystyrene );

std::cout << "standardDeviation: " << result->standardDeviation( 0 ) ;

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))

result = imagedev.measure_gaussian_noise(polystyrene)

print("standardDeviation: ", str(result.standard_deviation(0)))

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );

GaussianNoiseMsr result = Processing.MeasureGaussianNoise( polystyrene );

Console.WriteLine(  "standardDeviation: " + result.standardDeviation( 0 )  );