Multidimensional discrete convolution: Difference between revisions

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In signal processing, '''multidimensional convolution''' refers to the mathematical operation between two functions ''f'' and ''g'' of ''n''-dimensions that produces a third function, also of ''n''-dimensions. Multidimensional convolution is a direct extension of the one-dimensional [[Convolution|convolution]] case.

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This convolution

<ref>{{cite journal|last1=Fernandez|first1=Joseph|last2=Kumar|first2=Vijaya|title=Multidimensional Overlap-Add and Overlap-Save for Correlation and Convolution|journal=IEEE|date=Sept. 2013|issue=Image Processing (ICIP)|pages=509-513|doi=10.1109/ICIP.2013.6738105}}</ref>{{Multiple issues|{{dead end|date=November 2015}}{{orphan|date=November 2015}}{{one source|date=November 2015}}{{technical|date=November 2015}}}}

==The Helix Transform==

Similar to row-column decomposition, the helix transform computes the multidimensional convolution by incorporating one-dimensional convolutional properties and operators. Instead of using the separability of signals,