Correction Algorithm of Sampling Effect and Its Application
This addresses errors in signal acquisition for imaging systems, but it appears incremental as it builds on existing interpolation methods.
The paper tackled the sampling effect in imaging acquisition devices by proposing a correction algorithm, achieving accuracy increases of up to 106 for Gaussian images and 105 for digitized images with Shannon interpolation.
The sampling effect of the imaging acquisition device is long considered to be a modulation process of the input signal, introducing additional error into the signal acquisition process. This paper proposes a correction algorithm for the modulation process that solves the sampling effect with high accuracy. We examine the algorithm with perfect continuous Gaussian images and selected digitized images, which indicate an accuracy increase of 106 for Gaussian images, 102 at 15 times of Shannon interpolation for digitized images, and 105 at 101 times of Shannon interpolation for digitized images. The accuracy limit of the Gaussian image comes from the truncation error, while the accuracy limit of the digitized images comes from their finite resolution, which can be improved by increasing the time of Shannon interpolation.