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Kernel (image processing) 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. Or more simply, when each pixel in the output image is a function of the nearby pixels (including ...
Example of 3 median filters of varying radiuses applied to the same noisy photograph. The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image).
Basics of image editing. Raster images are stored in a computer in the form of a grid of picture elements, or pixels. These pixels contain the image's color and brightness information. Image editors can change the pixels to enhance the image in many ways. The pixels can be changed as a group, or individually, by the sophisticated algorithms ...
Unsharp masking ( USM) is an image sharpening technique, first implemented in darkroom photography, but now commonly used in digital image processing software. Its name derives from the fact that the technique uses a blurred, or "unsharp", negative image to create a mask of the original image. The unsharp mask is then combined with the original ...
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. The visual effect of this blurring ...
Let's Enhance. Let's Enhance [1] is a Ukrainian start-up [2] which develops an online service driven by artificial intelligence which allows improving images and zooming them without losing quality. [3] According to the developers, [4] they used the super-resolution technology of machine learning. The neural network, trained on a large base of ...
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