1984Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Sampling And Aliasing Problems With Imaging Arrays

Andreas Nordbryhn

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Abstract

In solid state imaging arrays aliasing is most certain to occur, unless some sort of presampling filters are used. A presampling filter would be an optical low-pass filter. Using incoherent light, there is a limit to how ideal such a filter can be made. Loss of contrast will always occur during prefiltering. Therefore there will be a trade-off between aliasing and contrast loss. A review of this problem is given. It is argued that with some applications, aliasing is not very harmful. This corresponds well to the common, practical use of solid state imagers, where no anti-aliasing filters are applied.

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What this paper is about

In solid state imaging arrays aliasing is most certain to occur, unless some sort of presampling filters are used. A presampling filter would be an optical low-pass filter. Using incoherent light, there is a limit to how ideal such a filter can be made. Loss of contrast will always occur during prefiltering. Therefore there will be a trade-off between aliasing and contrast loss. A review of this problem is given. It is argued that with some applications, aliasing is not very harmful. This corresponds well to the common, practical use of solid state imagers, where no anti-aliasing filters are applied.

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Available abstract

In solid state imaging arrays aliasing is most certain to occur, unless some sort of presampling filters are used. A presampling filter would be an optical low-pass filter. Using incoherent light, there is a limit to how ideal such a filter can be made. Loss of contrast will always occur during prefiltering. Therefore there will be a trade-off between aliasing and contrast loss. A review of this problem is given. It is argued that with some applications, aliasing is not very harmful. This corresponds well to the common, practical use of solid state imagers, where no anti-aliasing filters are applied.

Key concepts: Aliasing, Anti-aliasing, Anti-aliasing filter, Computer science, Filter (signal processing), Sampling (signal processing), Contrast (vision), Limit (mathematics)

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