Anti-aliasing

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In digital signal processing, spatial anti-aliasing is the technique of minimizing the distortion artifacts known as aliasing when representing a high-resolution image at a lower resolution. Anti-aliasing is used in digital photography, computer graphics, digital audio, and many other applications.

Anti-aliasing means removing signal components that have a higher frequency than is able to be properly resolved by the recording (or sampling) device. This removal is done before (re)sampling at a lower resolution. When sampling is performed without removing this part of the signal, it causes undesirable artifacts such as the black-and-white noise near the top of figure 1-a below.

In signal acquisition and audio, anti-aliasing is often done using an analog anti-aliasing filter to remove the out-of-band component of the input signal prior to sampling with an analog-to-digital converter. In digital photography, optical anti-aliasing filters are made of birefringent materials, and smooth the signal in the spatial optical domain. The anti-aliasing filter essentially blurs the image slightly in order to reduce resolution to below the limit of the digital sensor (the larger the pixel pitch, the lower the achievable resolution at the sensor level).

See the articles on signal processing and aliasing for more information about the theoretical justifications for anti-aliasing.

Contents

Examples

Figure 1-a illustrates the visual distortion that occurs when anti-aliasing is not used. Notice that near the top of the image, where the checkerboard is very distant, the image is difficult to recognize and is not aesthetically appealing. In contrast, Figure 1-b shows an anti-aliased version of the scene. The checkerboard near the top blends into gray, which is usually the desired effect when the resolution is insufficient to show the detail. Even near the bottom of the image, the edges appear much smoother in the anti-aliased image. Figure 1-c shows another anti-aliasing algorithm, based on the sinc filter, which is considered better than the algorithm used in 1-b.[1]

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