/// <summary>
/// Guided filer function.
/// </summary>
/// <param name="input">Input signal</param>
/// <param name="r">Filter size</param>
/// <param name="eps">Epsilon (0, 1)</param>
internal static void guidedfilter(Complex32[,] input, int r, float eps)
{
// Input signal properties:
int l0 = input.GetLength(0), l1 = input.GetLength(1), i, j;
// Calculating μ(I) and μ(I^2):
Complex32[,] x = (Complex32[, ])input.Clone();
Complex32[,] y = Matrice.Pow(input, 2.0f);
// Applying fast box filter:
x = x.Mean(r, r);
y = y.Mean(r, r);
// Calculating cov(I):
// This is the covariance of input in each local patch:
Complex32[,] c = new Complex32[l0, l1];
for (i = 0; i < l0; i++)
{
for (j = 0; j < l1; j++)
{
c[i, j] = y[i, j] - x[i, j] * x[i, j];
}
}
// Calculating μ(a) and μ(b):
Complex32[,] a = new Complex32[l0, l1];
Complex32[,] b = new Complex32[l0, l1];
for (i = 0; i < l0; i++)
{
for (j = 0; j < l1; j++)
{
a[i, j] = c[i, j] / (c[i, j] + eps);
b[i, j] = x[i, j] - a[i, j] * x[i, j];
}
}
// Applying fast box filter:
a = a.Mean(r, r);
b = b.Mean(r, r);
// Calculating μ(a) * I + μ(b):
for (i = 0; i < l0; i++)
{
for (j = 0; j < l1; j++)
{
input[i, j] = a[i, j] * input[i, j] + b[i, j];
}
}
return;
}
/// <summary>
///
/// </summary>
/// <param name="u">Matrix</param>
/// <param name="radius">Radius</param>
/// <returns>Matrix</returns>
internal static Complex32[] downsample(Complex32[] u, int radius)
{
int r = u.Length;
int n = (r + 1) / 2;
int i, k;
Complex32[] v = new Complex32[n];
for (k = 0, i = 0; i < r; i += 2, k++)
{
v[k] = u[i];
}
return(v.Mean(radius));
}
/// <summary>
///
/// </summary>
/// <param name="u">Array</param>
/// <param name="radius">Radius</param>
/// <returns>Array</returns>
internal static Complex32[] upsample(Complex32[] u, int radius)
{
int r = u.GetLength(0);
int n = r * 2;
int i, k;
Complex32[] v = new Complex32[n];
for (k = 0, i = 0; i < r; i++, k += 2)
{
v[k] = u[i];
v[k + 1] = u[i];
}
return(v.Mean(radius));
}
/// <summary>
///
/// </summary>
/// <param name="u">Matrix</param>
/// <param name="radius">Radius</param>
/// <returns>Matrix</returns>
internal static Complex32[,] downsample(Complex32[,] u, int radius)
{
int r = u.GetLength(0);
int c = u.GetLength(1);
int n = (r + 1) / 2, m = (c + 1) / 2;
int i, j, k, l;
Complex32[,] v = new Complex32[n, m];
for (k = 0, i = 0; i < r; i += 2, k++)
{
for (l = 0, j = 0; j < c; j += 2, l++)
{
v[k, l] = u[i, j];
}
}
return(v.Mean(radius, radius));
}
/// <summary>
/// Guided filer function.
/// </summary>
/// <param name="input">Input signal</param>
/// <param name="r">Filter size</param>
/// <param name="eps">Epsilon (0, 1)</param>
internal static void guidedfilter(Complex32[] input, int r, float eps)
{
// Input signal properties:
int length = input.Length, i;
// Calculating μ(I) and μ(I^2):
Complex32[] x = (Complex32[])input.Clone();
Complex32[] y = Matrice.Pow(input, 2.0f);
// Applying fast box filter:
x = x.Mean(r);
y = y.Mean(r);
// Calculating cov(I):
// This is the covariance of input in each local patch:
Complex32[] c = new Complex32[length];
for (i = 0; i < length; i++)
{
c[i] = y[i] - x[i] * x[i];
}
// Calculating μ(a) and μ(b):
Complex32[] a = new Complex32[length];
Complex32[] b = new Complex32[length];
for (i = 0; i < length; i++)
{
a[i] = c[i] / (c[i] + eps);
b[i] = x[i] - a[i] * x[i];
}
// Applying fast box filter:
a = a.Mean(r);
b = b.Mean(r);
// Calculating μ(a) * I + μ(b):
for (i = 0; i < length; i++)
{
input[i] = a[i] * input[i] + b[i];
}
return;
}
/// <summary>
///
/// </summary>
/// <param name="u">Matrix</param>
/// <param name="radius">Radius</param>
/// <returns>Matrix</returns>
internal static Complex32[,] upsample(Complex32[,] u, int radius)
{
int r = u.GetLength(0), c = u.GetLength(1);
int n = r * 2, m = c * 2;
int i, j, k, l;
Complex32[,] v = new Complex32[n, m];
for (k = 0, i = 0; i < r; i++, k += 2)
{
for (l = 0, j = 0; j < c; j++, l += 2)
{
v[k + 1, l] = u[i, j];
v[k, l + 1] = u[i, j];
v[k, l] = u[i, j];
v[k + 1, l + 1] = u[i, j];
}
}
return(v.Mean(radius, radius));
}
