Self-Adaptive Pre-Filtering Reconstruction for Optical Computed Tomography with Noisy Data

Abstract

Optical computed tomography (OCT) is a kind of technique that has been widely adopted to reconstruct three-dimensional distributions of physical parameters of various kinds of fluid fields, such as flame, plasma, etc. In most cases, projection data are often stained by noises due to environmental disturbance, instrumental inaccuracy, and other random interruptions. To improve the reconstruction performance of traditional iterative computed tomography algorithms, a self-adaptive pre-filtering approach was used to denoise before iteration. Firstly, a frequency domain statistic method was taken to evaluate levels of noises approximately. Then the cut-off frequency of a Butterworth lowpass filter was fixed based on the evaluated noise energy. The results show traditional iterative algorithms are obviously improved with pre-filtering approach in the case of noisy data reconstructions.