The Origin Family:

More Options:

 

Deconvolution using the FFT

Deconvolution is a process that undoes the "blurring" obtained after convoluting data. Its main purpose is to remove the effect of system response on a signal. In mathematical terms, while convolution is the product of the signal and response data sets, deconvolution is achieved by dividing the known convolution by the response data set.

Deconvolution in Origin...

In order to perform deconvolution in Origin, the convoluted data set should be on the left and the response data set on the right. The response data set should meet the following requirements:

  

Click on the image to see an example
  1. The response data set should consist of an odd number of points and be a representative sample of a symmetric function.
  2. The number of points in the response data set must be less than half the number of points in the convoluted data set.
  3. The sum of the points in the response curve should be unity in order to retain the amplitude of the original data set.

To avoid possible artifacts from the FFT (performed as part of the deconvolution process), the convoluted data should be padded with zero values until the number of points is equal to an integral power of two. The X data must be extended accordingly.

Once the data is set up properly, the deconvolution is performed by highlighting both data sets and selecting the Analysis:Deconvolute menu. Origin adds two columns to the rightmost position in the worksheet. The left column holds the index variables, and the right column holds the deconvolution result.

View an example...

Other options...
Origin - Data Analysis - Home
Origin - Data Analysis - Convolution
Origin - Data Analysis - Deconvolution

OriginLab® Corporation © 1992-2003. All rights reserved.