In digital signal processing, aliasing is a phenomenon in which a reconstructed signal from samples of the original signal contains low frequency components that are not present in the original one. This is caused when, in the original signal, there are components at frequency exceeding a certain frequency called Nyquist frequency, <math display="inline">f_s / 2</math>, where <math display="inline">f_s</math> is the sampling frequency (undersampling). This is because typical reconstruction methods use low frequency components while there are a number of frequency components, called aliases, which sampling result in the identical sample. It also often refers to the distortion or artifact that results when a signal reconstructed from samples is different from the original continuous signal.

Aliasing can occur in signals sampled in time, for instance in digital audio or the stroboscopic effect, and is referred to as temporal aliasing. 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. Suitable reconstruction filtering should then be used when restoring the sampled signal to the continuous domain or converting a signal from a lower to a higher sampling rate. For spatial anti-aliasing, the types of anti-aliasing include fast approximate anti-aliasing (FXAA), multisample anti-aliasing (MSAA), and supersampling. Temporal anti-aliasing is a special case of MSAA where pixel samples are collected over multiple frames.

Description

thumb|300px|Dots in the sky due to spatial aliasing caused by [[halftone resized to a lower resolution]]

When a digital image is viewed, a reconstruction is performed by a display or printer device. If the image data is processed incorrectly during sampling or reconstruction, the reconstructed image will differ from the original image, and an alias is seen.

An example of spatial aliasing is the moiré pattern observed in a poorly pixelized image of a brick wall. Spatial anti-aliasing techniques avoid such poor pixelizations. Aliasing can be caused either by the sampling stage or the reconstruction stage; these may be distinguished by calling sampling aliasing prealiasing and reconstruction aliasing postaliasing.

Further reading

  • Pharr, Matt; Humphreys, Greg. (28 June 2010). Physically Based Rendering: From Theory to Implementation. Morgan Kaufmann. . Chapter 7 (Sampling and reconstruction). Retrieved 3 March 2013.
  • by Tektronix Application Engineer
  • Anti-Aliasing Filter Primer by La Vida Leica, discusses its purpose and effect on recorded images
  • Interactive examples demonstrating the aliasing effect