A Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales—see scale space representation and scale space implementation.
Mathematically, applying a Gaussian blur to an image is the same as convolving the image with a Gaussian function; this is also known as a two-dimensional Weierstrass transform. By contrast, convolving by a circle (i.e., a circular box blur) would more accurately reproduce the bokeh effect. Since the Fourier transform of a Gaussian is another Gaussian, applying a Gaussian blur has the effect of reducing the image's high-frequency components; a Gaussian blur is thus a low pass filter.
Read more about Gaussian Blur: Mechanics, Low-pass Filter, Sample Gaussian Matrix, Implementation, Common Uses
Famous quotes containing the word blur:
“Television ... helps blur the distinction between framed and unframed reality. Whereas going to the movies necessarily entails leaving one’s ordinary surroundings, soap operas are in fact spatially inseparable from the rest of one’s life. In homes where television is on most of the time, they are also temporally integrated into one’s “real” life and, unlike the experience of going out in the evening to see a show, may not even interrupt its regular flow.”
—Eviatar Zerubavel, U.S. sociologist, educator. The Fine Line: Making Distinctions in Everyday Life, ch. 5, University of Chicago Press (1991)