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multidimensional signal processing : ウィキペディア英語版
multidimensional signal processing
In signal processing, multidimensional signal processing covers all signal processing done using multidimensional signals and systems. While multidimensional signal processing is a subset of signal processing, it is unique in the sense that it deals specifically with data that can only be adequately detailed using more than one dimension. In m-D digital signal processing, useful data is sampled in more than one dimension. Examples of this are image processing and multi-sensor radar detection. Both of these examples use multiple sensors to sample signals and form images based on the manipulation of these multiple signals.
Processing in multi-dimension (m-D) requires more complex algorithms, compared to the 1-D case, to handle calculations such as the Fast Fourier Transform due to more degrees of freedom.〔D. Dudgeon and R. Mersereau, Multidimensional Digital Signal Processing, Prentice-Hall, First Edition, pp. 2, 1983.〕 In some cases, m-D signals and systems can be simplified into single dimension signal processing methods, if the considered systems are separable.
Typically, multidimensional signal processing is directly associated with digital signal processing because its complexity warrants the use of computer modelling and computation.〔 A multidimensional signal is similar to a single dimensional signal as far as manipulations that can be performed, such as sampling, Fourier analysis, and filtering. The actual computations of these manipulations grow with the number of dimensions.
== Sampling ==
(詳細はlattice, which is a "pattern" based on the sampling vectors of the m-D data set.〔Mersereau, R.; Speake, T., "The processing of periodically sampled multidimensional signals," Acoustics, IEEE Transactions on Speech and Signal Processing, vol.31, no.1, pp.188-194, Feb 1983.〕 These vectors can be single dimensional or multidimensional depending on the data and the application.〔
Multidimensional sampling is similar to classical sampling as it must adhere to the Nyquist–Shannon sampling theorem. It is affected by aliasing and considerations must be made for eventual reconstruction.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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