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Convolution

Apply a convolution kernel to an image.

Overview

Convolution is a common image-processing technique that changes the value of a pixel according to the values of its surrounding pixels. Many common image filters, such as blurring, detecting edges, sharpening, and embossing, derive from convolution.

Kernels form the basis of convolution operations. Kernels are arrays or matrices of weights that indicate the influence of a pixel’s neighbors on its final value. To calculate the value of each transformed pixel, a convolution operation adds the products of each surrounding pixel value with the corresponding kernel value. During a convolution operation, the kernel passes over every pixel in the image, repeating this procedure, and then applies the effect to the entire image.

Topics

Convolving an 8-bit image with 32-bit weights

Convolving with separable filter kernels

Convolving without bias

Convolving with bias

Convolving with multiple kernels

Convolving with high-speed box and tent filters

Deconvolving

See Also

Convolution and Morphology