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This measures the degree to which the design avoids aliasing between main effects and important interactions. [ 5 ] Fractional factorial experiments have long been a basic tool in agriculture, [ 6 ] food technology, [ 7 ] [ 8 ] industry, [ 9 ] [ 10 ] [ 11 ] medicine and public health, [ 12 ] [ 13 ] and the social and behavioral sciences. [ 14 ]
Aliasing in spatially sampled signals (e.g., moiré patterns in digital images) is referred to as spatial aliasing. Aliasing is generally avoided by applying low-pass filters or anti-aliasing filters (AAF) to the input signal before sampling and when converting a signal from a higher to a lower sampling rate.
An anti-aliasing filter (AAF) is a filter used before a signal sampler to restrict the bandwidth of a signal to satisfy the Nyquist–Shannon sampling theorem over the band of interest. Since the theorem states that unambiguous reconstruction of the signal from its samples is possible when the power of frequencies above the Nyquist frequency is ...
Conservative morphological anti-aliasing (CMAA), a type of spatial anti-aliasing method [2] Temporal anti-aliasing (TAA) in CGI, techniques to reduce or remove the effects of temporal aliasing in moving images Deep learning anti-aliasing (DLAA), a type of spatial and temporal anti-aliasing method relying on dedicated tensor core processors
Temporal anti-aliasing (TAA) is a spatial anti-aliasing technique for computer-generated video that combines information from past frames and the current frame to remove jaggies in the current frame. In TAA, each pixel is sampled once per frame but in each frame the sample is at a different location within the frame.
When the lower level aliasing is suppressed, to make the third image and then that is down-sampled once more, without anti-aliasing, to make the fifth image, the order on the scale of the third image appears as systematic aliasing in the fifth image. Pure down-sampling of an image has the following effect (viewing at full-scale is recommended):
Deep Learning Anti-Aliasing (DLAA) is a form of spatial anti-aliasing created by Nvidia. [1] DLAA depends on and requires Tensor Cores available in Nvidia RTX cards. [1]DLAA is similar to Deep Learning Super Sampling (DLSS) in its anti-aliasing method, [2] with one important differentiation being that the goal of DLSS is to increase performance at the cost of image quality, [3] whereas the ...
A naive approach to anti-aliasing the line would take an extremely long time. Wu's algorithm is comparatively fast, but is still slower than Bresenham's algorithm. The algorithm consists of drawing pairs of pixels straddling the line, each coloured according to its distance from the line.