Convolution and correlation are central to many kinds of operations on lists of data. They are used in such areas as signal and image processing, statistical data analysis, ...

Mathematica provides built-in functions for generating standard structure matrices and convolution kernels in any number of dimensions, in a form that can be used directly in ...

Constructing matrices with special shapes. This creates a matrix of 0s containing a radius 4 diamond of 1s. The result is a 9×9 matrix. The size of the matrix can be ...

DiscreteConvolve[f, g, n, m] gives the convolution with respect to n of the expressions f and g. DiscreteConvolve[f, g, {n_1, n_2, ...}, {m_1, m_2, ...}] gives the ...

Convolve[f, g, x, y] gives the convolution with respect to x of the expressions f and g.Convolve[f, g, {x_1, x_2, ...}, {y_1, y_2, ...}] gives the multidimensional ...

DirichletConvolve[f, g, n, m] gives the Dirichlet convolution of the expressions f and g.

ListLineIntegralConvolutionPlot[{array, image}] generates a line integral convolution plot of image convolved with the vector field defined by an array of vector field ...

LineIntegralConvolutionScale is an option to LineIntegralConvolutionPlot and related functions that determines the scale of the line integral convolution to be used.

CUDAImageConvolve[img, kern] gives the convolution of img with kern.CUDAImageConvolve[list, kern] gives the convolution of list with kern.CUDAImageConvolve[mem, kern] gives ...

ImageConvolve[image, ker] gives the convolution of image with kernel ker.