Explore: Gaussian Blur

In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician

disk, a more computationally intensive operation than the "standard" Gaussian blur; the former produces sharp circles around highlights whereas the latter

algorithm. Box blurs are frequently used to approximate a Gaussian blur. By the central limit theorem, repeated application of a box blur will approximate

theory Full width at half maximum Gaussian blur – convolution, which uses the normal distribution as a kernel Gaussian function Modified half-normal distribution

Gaussian filters, in image processing where two-dimensional Gaussians are used for Gaussian blurs, and in mathematics to solve heat equations and diffusion

original image I ( x , y ) {\displaystyle I\left(x,y\right)} with the Gaussian blur G ( x , y , k σ ) {\displaystyle G\left(x,y,k\sigma \right)} at scale

example. For small to moderate levels of Gaussian noise, the median filter is demonstrably better than Gaussian blur at removing noise whilst preserving edges

processing, a Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to it, since a true Gaussian response would

science, difference of Gaussians (DoG) is a feature enhancement algorithm that involves the subtraction of one Gaussian blurred version of an original

-1, 0, -1, 5, -1, 0, -1, 0) #define box_blur mat3(1, 1, 1, 1, 1, 1, 1, 1, 1) * 0.1111 #define gaussian_blur mat3(1, 2, 1, 2, 4, 2, 1, 2, 1) * 0.0625