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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.
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.
Specifically, unsharp masking is a simple linear image operation—a convolution by a kernel that is the Dirac delta minus a gaussian blur kernel. Deconvolution, on the other hand, is generally considered an ill-posed inverse problem that is best solved by nonlinear approaches.
An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter.
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.
A miniature can also be simulated digitally, using an image editor to blur the top and bottom of the photograph, so that only the subject is sharp. With basic techniques, e.g., a tool such as Adobe Photoshop 's Lens Blur filter, [ 9 ] using sharpness gradients extending from the middle of the image to the top and bottom, the effect is quite ...