Recursive Implementation of the Gaussian Filter

Question

I'm trying to implement an IIR form to approximate the Gaussian Blur Filter.
I'm working with the article "Recursive Implementation of the Gaussian Filter" by Ian T. Young and Lucas J. van Vliet.

They suggest a form and way to calculate the coefficients as given by:

I'm trying to reproduce their example for $ q = 5.0 $.

There is the MATLAB code I wrote:

qFactor = 5;

b0Coeff = 1.57825 + (2.44413 * qFactor) + (1.4281 * qFactor * qFactor) + (0.422205 * qFactor * qFactor * qFactor);
b1Coeff = (2.44413 * qFactor) + (2.85619 * qFactor * qFactor) + (1.26661 * qFactor * qFactor * qFactor);
b2Coeff = (-1.4281 * qFactor * qFactor) + (-1.26661 * qFactor * qFactor * qFactor);
b3Coeff = 0.422205 * qFactor * qFactor * qFactor;

normalizationCoeff = 1 - ((b1Coeff + b2Coeff + b3Coeff) / b0Coeff);

vDenCoeff = [b0Coeff, b1Coeff, b2Coeff, b3Coeff] / b0Coeff;


vXSignal = zeros(61, 1);
vXSignal(31) = 10;

vYSignal = filter(normalizationCoeff, vDenCoeff, vXSignal);
vYSignal = filter(normalizationCoeff, vDenCoeff, vYSignal(end:-1:1));

figure();
plot(vYSignal);

Now, the result I get is this:

Namely, I get a filter which isn't stable.
Yet it seems I get the same coefficients as they get in their example.

What am I missing?
Has anyone ever implemented this method?

Thank You.

Answer 1

The answer was simple, the article uses the coefficients value on one hand where the MATLAB implementation on the other.
Namely, a minus sign should be added.

Here's the correct code:

qFactor = 5;

b0Coeff = 1.57825 + (2.44413 * qFactor) + (1.4281 * qFactor * qFactor) + (0.422205 * qFactor * qFactor * qFactor);
b1Coeff = (2.44413 * qFactor) + (2.85619 * qFactor * qFactor) + (1.26661 * qFactor * qFactor * qFactor);
b2Coeff = (-1.4281 * qFactor * qFactor) + (-1.26661 * qFactor * qFactor * qFactor);
b3Coeff = 0.422205 * qFactor * qFactor * qFactor;

normalizationCoeff = 1 - ((b1Coeff + b2Coeff + b3Coeff) / b0Coeff);

vDenCoeff = [b0Coeff, -b1Coeff, -b2Coeff, -b3Coeff] / b0Coeff;


vXSignal = zeros(61, 1);
vXSignal(31) = 10;

vYSignal = filter(normalizationCoeff, vDenCoeff, vXSignal);
vYSignal = filter(normalizationCoeff, vDenCoeff, vYSignal(end:-1:1));

figure();
plot(vYSignal);