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

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.