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The difference between a small and large 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 and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail.
In 2013, development started on a rewritten version known as OBS Multiplatform (later renamed OBS Studio) for multi-platform support, a more thorough feature set, and a more powerful API. [17] In 2016, OBS "Classic" lost support and OBS Studio became the primary version. [18] In March 2022, OBS was released on Steam for both Windows and Mac. [19]
Boris Continuum Complete is a special effects Plug-in package that works in conjunction with Adobe Creative Suite, including CS6, Avid editing and finishing systems such as: Sony Vegas Pro, and Apple Final Cut Pro.
For small to moderate levels of Gaussian noise, the median filter is demonstrably better than Gaussian blur at removing noise whilst preserving edges for a given, fixed window size. [5] However, its performance is not that much better than Gaussian blur for high levels of noise, whereas, for speckle noise and salt-and-pepper noise (impulsive ...
A number of optimizations can be applied when implementing the box blur of a radius r and N pixels: [6] The box blur is a separable filter, so that only two 1D passes of averaging 2 r + 1 pixels will be needed, one horizontal and one vertical, for each pixel.
In image processing, a kernel, convolution matrix, or mask is a small matrix used for blurring, sharpening, embossing, edge detection, and more.This is accomplished by doing a convolution between the kernel and an image.
This integral is 1 if and only if = (the normalizing constant), and in this case the Gaussian is the probability density function of a normally distributed random variable with expected value μ = b and variance σ 2 = c 2: = (()).
When utilized for image enhancement, the difference of Gaussians algorithm is typically applied when the size ratio of kernel (2) to kernel (1) is 4:1 or 5:1. In the example images, the sizes of the Gaussian kernels employed to smooth the sample image were 10 pixels and 5 pixels.