Complex-domain SVD- and sparsity-based denoising for optical diffraction tomography

Autor:	Igor Shevkunov, Michał Ziemczonok, Małgorzata Kujawińska, Karen Egiazarian
Přispěvatelé:	Tampere University, Computing Sciences
Rok vydání:	2022
Předmět:	Mechanical Engineering Electrical and Electronic Engineering 113 Computer and information sciences Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials
Zdroj:	Optics and Lasers in Engineering. 159:107228
ISSN:	0143-8166
Popis:	In this paper, we adopt a complex-domain cube filter (CCF) developed for hyperspectral 3D complex domain images for noise suppression of 3D complex-valued data in optical diffraction tomography. CCF is based on two processing steps: singular value decomposition (SVD) and complex-domain sparsity-based filter (CDID). SVD provides data compression and CDID noise suppression in the compressed domain. We demonstrate that the CCF algorithm can be used to denoise captured projections (sinogram), which results in enhanced tomographic reconstruction. The accuracy and quantitative advantage of CCF application are shown in simulation tests and in the processing of the experimental data. We show that the algorithm effectively suppresses noise and retrieves objects’ details even for highly noisy data. publishedVersion
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c42a56e5d128139f98901ee64c251ba2 https://doi.org/10.1016/j.optlaseng.2022.107228 Zobrazit plný text záznamu Full Text from ScienceDirect