DECONVOLUTE function

Syntax: yd = DECONVOLUTE(y,b)

The DECONVOLUTE function accepts two vector arguments. It deconvolutes the first argument vector, y, with the specified blurring vector, b. The result is a vector the same length as y. The blurring vector b, the second argument, must contain an even number of points. The preferred lengths are powers of 2. The deconvolution is done using fast Fourier transforms. The following restrictions apply:

Noise in b produces a change in the output, which, due to averaging, has a small effect. Noise effects depend on the shape of the deconvoluted peak. The narrower this peak, the more effect the noise in b has. This occurs because each noisy point becomes a greater percentage of the total number in the deconvoluting function, thus reducing the average effect. In many applications, the noise in the measured data is statistical in nature and so, to reduce the sensitivity to this noise on the deconvolution, apply smoothing filters on the measured data before deconvolution.

The CONVOLUTE function calculates the convolution.

  Dawson's integral
  Derivative