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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 itself) in the input image ...
This is the standard blend mode which uses the top layer alone, [3] without mixing its colors with the layer beneath it: [example needed] f ( a , b ) = b {\displaystyle f(a,b)=b} where a is the value of a color channel in the underlying layer, and b is that of the corresponding channel of the upper layer.
In computer science, a mask or bitmask is data that is used for bitwise operations, particularly in a bit field.Using a mask, multiple bits in a byte, nibble, word, etc. can be set either on or off, or inverted from on to off (or vice versa) in a single bitwise operation.
One of the simpler ways of increasing the size, replacing every pixel with a number of pixels of the same color. The resulting image is larger than the original, and preserves all the original detail, but has (possibly undesirable) jaggedness. The diagonal lines of the "W", for example, now show the "stairway" shape characteristic of nearest ...
A color spectrum image with an alpha channel that falls off to zero at its base, where it is blended with the background color.. In computer graphics, alpha compositing or alpha blending is the process of combining one image with a background to create the appearance of partial or full transparency. [1]
Demosaicing (or de-mosaicing, demosaicking), also known as color reconstruction, is a digital image processing algorithm used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color filter array (CFA) such as a Bayer filter. It is also known as CFA interpolation or debayering.
Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [2] navigation of mobile robots, [3] or edge detection in images.
For most dithering purposes, it is sufficient to simply add the threshold value to every pixel (without performing normalization by subtracting 1 ⁄ 2), or equivalently, to compare the pixel's value to the threshold: if the brightness value of a pixel is less than the number in the corresponding cell of the matrix, plot that pixel black ...