Spatial aliasing quantification and analysis of existing imaging sensors: NASA’s Hubble Space Telescope, OrbView-3 OHRIS, and commercial-off-the-shelf camera for autonomous robots

Abstract. Spatial aliasing in its most pronounced form is seen as a Moiré pattern in (sampled) images. Less dramatically, aliasing is a form of image quality (IQ) degradation and exists at some level within any (sampled) image. More sampling tends to improve IQ due to less aliasing; however, there are drawbacks. Spatial aliasing has been recently quantified by Mudge [Appl. Opt.62(13), 3260–3264 (2023)] for imaging sensors (optics plus detector). This quantification allows a trade to be made between the acceptable aliasing errors imbedded within the (sampled) image and the penalty, or cost, associated with additional sampling, e.g., increased complexity, data throughput and storage, and reduced signal-to-noise ratio for fixed arrays or increased scan time. In this work, we examine several existing and useful imaging sensors along with their imagery and aliasing errors to appreciate how well these existing systems are designed with respect to sampling to better inform how future systems could potentially be improved. Finally, from these analyses, a 2% aliasing error rule is extracted initiating a universal aliasing boundary.