/// <summary>
/// WARNING!!: This method implements a convolution and like all convolutions is very slow (unless it can be fully parellised);
/// Consider using another noise normalisation method such as in the class NoiseRemoval_Briggs.
///
/// This method does local contrast normalisation. Typically LCN normalises by division only and is motivated by what is known
/// to happen in the visual cortext.
/// Every matrix element or pixel value is divided by the (scaled) standard deviation of pixel values
/// in a local field centred on the pixel. Scaling affects the severity of the normalisation.
/// There are several ways of doing LCN. That is can divide by the local sum of squares. Or can calculate the local z-score
/// which effectively normalises by both subtraction and division.
/// This method is based on formula given by LeCun. Python code at the bottom of this class is the actual
/// code used by LeCun which appears to do something different.
/// Wish I knew Python!
///
/// </summary>
/// <param name="inputM"></param>
/// <param name="fieldSize"></param>
/// <returns></returns>
public static double[,] ComputeLCN(double[,] inputM, int fieldSize)
{
/*
* // FOLLOWING LINES ARE FOR DEBUGGING AND TESTING
* var inputM2 = MatrixTools.MatrixRotate90Anticlockwise(inputM);
* ImageTools.DrawReversedMatrix(inputM2, @"C:\SensorNetworks\Output\Sonograms\TESTMATRIX0.png");
* double fractionalStretching = 0.05;
* inputM2 = ImageTools.ContrastStretching(inputM2, fractionalStretching);
* ImageTools.DrawReversedMatrix(inputM2, @"C:\SensorNetworks\Output\Sonograms\TESTMATRIX1.png");
* */
int rowCount = inputM.GetLength(0);
int colCount = inputM.GetLength(1);
int frameWidth = fieldSize / 2;
/// add frame around matrix to compensate for edge effects.
double[,] framedM = MatrixTools.FrameMatrixWithZeros(inputM, frameWidth);
// output matrix is same size as input.
double[,] outputM = new double[rowCount, colCount];
double[,] subMatrix;
double NSquared = fieldSize * fieldSize;
// alpha is a scaling factor. LeCun set it = 0.00001. Here set much higher to have a noticeable effect!
double alpha = 1.0;
// convolve gaussian with the matrix
for (int r1 = 0; r1 < rowCount; r1++)
{
for (int c1 = 0; c1 < colCount; c1++)
{
// get field
int r2 = r1 + fieldSize - 1;
int c2 = c1 + fieldSize - 1;
subMatrix = MatrixTools.Submatrix(framedM, r1, c1, r2, c2);
double[] V = MatrixTools.Matrix2Array(subMatrix);
double av, variance;
NormalDist.AverageAndVariance(V, out av, out variance);
double numerator = inputM[r1, c1];
//double numerator = (inputM[r1, c1] - av);
double denominator = Math.Sqrt(1 + (alpha * variance));
outputM[r1, c1] = numerator / denominator;
}
}
// FOLLOWING LINES ARE FOR DEBUGGING AND TESTING
/*
* outputM = MatrixTools.MatrixRotate90Anticlockwise(outputM);
* ImageTools.DrawReversedMatrix(outputM, @"C:\SensorNetworks\Output\Sonograms\TESTMATRIX2.png");
* double fractionalStretching = 0.05;
* outputM = ImageTools.ContrastStretching(outputM, fractionalStretching);
* ImageTools.DrawReversedMatrix(outputM, @"C:\SensorNetworks\Output\Sonograms\TESTMATRIX3.png");
*/
return(outputM);
}