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  2. Anisotropic filtering - Wikipedia

    en.wikipedia.org/wiki/Anisotropic_filtering

    An example of anisotropic mipmap image storage: the principal image on the top left is accompanied by filtered, linearly transformed copies of reduced size. Isotropic mipmap of the same image. Anisotropic filtering enhances texture sharpness, counteracting the blur introduced by mipmapping, a common anti-aliasing technique.

  3. Kernel (image processing) - Wikipedia

    en.wikipedia.org/wiki/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.

  4. Unsharp masking - Wikipedia

    en.wikipedia.org/wiki/Unsharp_masking

    For image processing, deconvolution is the process of approximately inverting the process that caused an image to be blurred. Specifically, unsharp masking is a simple linear image operation—a convolution by a kernel that is the Dirac delta minus a gaussian blur kernel.

  5. Edge enhancement - Wikipedia

    en.wikipedia.org/wiki/Edge_enhancement

    Edge enhancement is an image processing filter that enhances the edge contrast of an image or video in an attempt to improve its acutance (apparent sharpness).. The filter works by identifying sharp edge boundaries in the image, such as the edge between a subject and a background of a contrasting color, and increasing the image contrast in the area immediately around the edge.

  6. Microsoft PowerPoint - Wikipedia

    en.wikipedia.org/wiki/Microsoft_PowerPoint

    PowerPoint 2013 and 2016 will also save a presentation in many other file formats, including PDF format, MPEG-4 or WMV video, as a sequence of single-picture files (using image formats including GIF, JPEG, PNG, TIFF, and some older formats), and as a single presentation file in which all slides are replaced with pictures. PowerPoint will both ...

  7. Gaussian blur - Wikipedia

    en.wikipedia.org/wiki/Gaussian_blur

    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.