Interactive Denoising of 3D Volumes Using Wavelets
Fraunhofer-Institut für Integrierte Schaltungen IIS, Thomas Lang, Andreas Michael Stock · e-Journal of Nondestructive Testing · 2023
Conventional signal denoising methods either focus on statistical properties or employ a (complicated) set of parameters a user needs to tweak appropriately. More recently, AI-based denoising methods are increasingly employed due to their robustness. However, these methods typically require a large number of training datasets, which are costly to produce in a computed tomography scenario. This work introduces a denoising procedure which combines classifier-based decision making with interactive user input in the wavelet domain. These interactive user inputs allows for an easy and straight-forward specification of noisy regions, which works especially well in the wavelet basis, without the need for statistical properties or large numbers of parameters. At the same time, the obtained results show that noise in the scanned data is reduced considerably while keeping the structural similarity intact.