Linearly-sampled Gaussian blur kernel

Kernel.cs

class Kernel
{
    public double[] Weights;
    public double[] Offsets;

    public Kernel(int taps)
    {
        Debug.Assert(taps >= 0);

        double[] row = genPascalRow(taps);

        double sum = row.Sum();

        List<double> weights = new List<double>();
        List<double> offsets = new List<double>();

        if (row.Length % 2 == 0)
        {
            float o = -0.5f;
            for (int i = row.Length / 2 - 1, count = 0; i <= row.Length - 2; i += 2, o++, count++)
            {
                weights.Add((row[i] + row[i + 1]) / sum);
                offsets.Add((o * row[i] + (o + 1) * row[i + 1]) / sum / weights[count]);
            }
        }
        else
        {
            weights.Add(row[row.Length / 2] / sum);
            offsets.Add(0.0);
            for (int i = row.Length / 2 + 1, o = 1, count = 1; i < row.Length - 2; i += 2, o++, count++)
            {
                weights.Add((row[i] + row[i + 1]) / sum);
                offsets.Add((o * row[i] + (o + 1) * row[i + 1]) / sum / weights[count]);
            }
        }

        Weights = weights.ToArray();
        Offsets = offsets.ToArray();
    }

    private static double[] genPascalRow(int n)
    {
        double[] row = new double[n + 1];
        row[0] = 1;

        for (int i = 0; i < n; i++)
            row[i + 1] = row[i] * (n - i) / (i + 1);

        return row;
    }
}