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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
In some circumstances, it is possible to correct some of the effects of chromatic aberration in digital post-processing. However, in real-world circumstances, chromatic aberration results in permanent loss of some image detail. Detailed knowledge of the optical system used to produce the image can allow for some useful correction. [15]
A demonstration of the effect of a neutral density filter. Note that the photograph was exposed for the view through the filter, and thus the remainder of the scene is overexposed. If the exposure had instead been set for the unfiltered background, it would appear properly exposed while the view through the filter would be dark. A set of ND filters
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.
From left to right: an original photo with no bokeh or blur; the same photo with synthetic bokeh effect applied to its background; the same photo with Gaussian blur applied to its background. Bokeh can be simulated by convolving the image with a kernel that corresponds to the image of an out-of-focus point source taken with a real camera.
The moiré effect also occurs between overlapping transparent objects. [5] For example, an invisible phase mask is made of a transparent polymer with a wavy thickness profile. As light shines through two overlaid masks of similar phase patterns, a broad moiré pattern occurs on a screen some distance away.
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