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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.
Sometimes, you find a drawing or similar image useful for a Wikipedia article, that was saved as a JPEG but should have been saved as a PNG.JPEG is good for images where the color changes fluidly throughout the image, like in a photograph, whereas PNG files are good for images with relatively few colors, such as a drawing of a flag, a chart, or a map; note that sometimes SVG is better.
In the simple case of grayscale images, the blurred images are obtained by convolving the original grayscale images with Gaussian kernels having differing width (standard deviations). Blurring an image using a Gaussian kernel suppresses only high-frequency spatial information. Subtracting one image from the other preserves spatial information ...
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.
an image that is not rectangular can be filled to the required rectangle using transparent surroundings; the image can even have holes (e.g. be ring-shaped) in a run of text, a special symbol for which an image is used because it is not available in the character set, can be given a transparent background, resulting in a matching background.
A popular way to create a composite image is to use transparent layers. The background image is used as the bottom layer, and the image with parts to be added are placed in a layer above that. Using an image layer mask, all but the parts to be merged is hidden from the layer, giving the
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