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  2. Texture filtering - Wikipedia

    en.wikipedia.org/wiki/Texture_filtering

    Mipmapping is a standard technique used to save some of the filtering work needed during texture minification. [2] It is also highly beneficial for cache coherency - without it the memory access pattern during sampling from distant textures will exhibit extremely poor locality, adversely affecting performance even if no filtering is performed.

  3. Gaussian blur - Wikipedia

    en.wikipedia.org/wiki/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.

  4. Gaussian filter - Wikipedia

    en.wikipedia.org/wiki/Gaussian_filter

    However, it is more common to define the cut-off frequency as the half power point: where the filter response is reduced to 0.5 (−3 dB) in the power spectrum, or 1/ √ 2 ≈ 0.707 in the amplitude spectrum (see e.g. Butterworth filter). For an arbitrary cut-off value 1/c for the response of the filter, the cut-off frequency is given by

  5. Trilinear filtering - Wikipedia

    en.wikipedia.org/wiki/Trilinear_filtering

    Trilinear filtering is an extension of the bilinear texture filtering method, which also performs linear interpolation between mipmaps. [1] [2]Bilinear filtering has several weaknesses that make it an unattractive choice in many cases: using it on a full-detail texture when scaling to a very small size causes accuracy problems from missed texels, and compensating for this by using multiple ...

  6. 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.

  7. Savitzky–Golay filter - Wikipedia

    en.wikipedia.org/wiki/Savitzky–Golay_filter

    (m − 1)/2 points at the start and end of the series cannot be calculated using this process. Various strategies can be employed to avoid this inconvenience. The data could be artificially extended by adding, in reverse order, copies of the first (m − 1)/2 points at the beginning and copies of the last (m − 1)/2

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