/// <summary>
/// Perform an inverse haar wavelet mel scaled transform. E.g. perform an ihaar2d and inverse Mel Filterbands and return stftdata
/// </summary>
/// <param name="wavelet">wavelet matrix</param>
/// <returns>matrix inverse wavelet'ed and mel removed (e.g. stftdata)</returns>
public Matrix InverseMelScaleWaveletPadding(ref Matrix wavelet)
{
using (new DebugTimer("InverseMelScaleWaveletPadding(wavelet)")) {
// 6. Perform the Inverse Wavelet Transform
Matrix mel = WaveletUtils.InverseHaarWaveletTransform2D(wavelet.MatrixData);
// Resize (remove padding)
mel = mel.Resize(melScaleFreqsIndex.Length - 2, wavelet.Columns);
// 5. Take Inverse Logarithm
// Divide with first triangle height in order to scale properly
for (int i = 0; i < mel.Rows; i++)
{
for (int j = 0; j < mel.Columns; j++)
{
mel.MatrixData[i][j] = Math.Pow(10, (mel.MatrixData[i][j] / 20)) / melScaleTriangleHeights[0];
}
}
// 4. Inverse Mel Scale using interpolation
// i.e. from e.g.
// mel=Rows: 40, Columns: 165 (average freq, time slice)
// to
// m=Rows: 1024, Columns: 165 (freq, time slice)
//Matrix m = filterWeights.Transpose() * mel;
var m = new Matrix(filterWeights.Columns, mel.Columns);
InverseMelScaling(mel, m);
return(m);
}
}
public static void TestDenoise(string imageInPath)
{
var image = ReadImageGrayscale(imageInPath);
// Normalize the pixel values to the range 0..1.0. It does this by dividing all pixel values by the max value.
double max = image.Max((b) => b.Max((v) => Math.Abs(v)));
double[][] imageNormalized = image.Select(i => i.Select(j => j / max).ToArray()).ToArray();
var normalizedMatrix = new Matrix(imageNormalized);
normalizedMatrix.DrawMatrixImage("lena-original.png", -1, -1, false);
// Add Noise using normally distributed pseudorandom numbers
// image_noisy = image_normalized + 0.1 * randn(size(image_normalized));
RandomUtils.Seed(Guid.NewGuid().GetHashCode());
double[][] imageNoisy = imageNormalized.Select(i => i.Select(j => j + (0.2 * RandomUtils.NextDouble())).ToArray()).ToArray();
var matrixNoisy = new Matrix(imageNoisy);
matrixNoisy.DrawMatrixImage("lena-noisy.png", -1, -1, false);
// Haar Wavelet Transform
Matrix haarMatrix = WaveletUtils.HaarWaveletTransform2D(imageNoisy);
// Thresholding
const double threshold = 0.10; // 0.15 seems to work well with the noise added above, 0.1
var yHard = Thresholding.PerformHardThresholding(haarMatrix.MatrixData, threshold);
var ySoft = Thresholding.PerformSoftThresholding(haarMatrix.MatrixData, threshold);
var ySemisoft = Thresholding.PerformSemisoftThresholding(haarMatrix.MatrixData, threshold, threshold * 2);
var ySemisoft2 = Thresholding.PerformSemisoftThresholding(haarMatrix.MatrixData, threshold, threshold * 4);
var yStrict = Thresholding.PerformStrictThresholding(haarMatrix.MatrixData, 100);
// Inverse 2D Haar Wavelet Transform
Matrix zHard = WaveletUtils.InverseHaarWaveletTransform2D(yHard);
Matrix zSoft = WaveletUtils.InverseHaarWaveletTransform2D(ySoft);
Matrix zSemisoft = WaveletUtils.InverseHaarWaveletTransform2D(ySemisoft);
Matrix zSemisoft2 = WaveletUtils.InverseHaarWaveletTransform2D(ySemisoft2);
Matrix zStrict = WaveletUtils.InverseHaarWaveletTransform2D(yStrict);
// Output the images
zHard.DrawMatrixImage("lena-thresholding-hard.png", -1, -1, false);
zSoft.DrawMatrixImage("lena-thresholding-soft.png", -1, -1, false);
zSemisoft.DrawMatrixImage("lena-thresholding-semisoft.png", -1, -1, false);
zSemisoft2.DrawMatrixImage("lena-thresholding-semisoft2.png", -1, -1, false);
zStrict.DrawMatrixImage("lena-thresholding-strict.png", -1, -1, false);
}
