Filtragem de projeções tomográficas da ciência do solo utilizando Kalman e redes neurais
Abstract
This work presents the space variant noise filtering of tomographic projections based on
the Kalman filter. For development and filter selection it was evaluated different
modalities of the Kalman filter, as well as included the use of Ascombe transform and
neural network. Results were analyzed by means of Improvement in Signal to Noise Ratio
(ISNR) measurements, which were obtained in a region of interest (ROI) on the resultant
images, reconstructed with the use of a backprojection algorithm. In this context the results
qualified the unscented Kalman filter with a neural network as the best configuration for
filtering of soil tomographic projections.