Improved regularization reconstruction from sparse angle data in optical diffraction tomography

Abstract

In this paper, we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography. Based on optical diffraction tomography and the deterministic regularization algorithm, the regularization iteration is performed in the space domain and the frequency domain simultaneously, which greatly reduces the computational cost. By applying piecewise-smoothness and positivity constraints as the penalty function, the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed. Using simulated and experimental results, we show that the proposed regularization algorithm allows accurate refractive index reconstruction from very sparse angle data in optical diffraction tomography.

Deep-Learning-Based Halo-Free White-Light Diffraction Phase Imaging

“…And the optical pathlength data can be further converted into various biologically-relevant information [2][3][4]. Kinds of QPI techniques, such as optical coherence tomography (OCT) [5,6], digital holographic microscopy (DHM) [7,8], diffraction phase microscopy (DPM) [9,10], transport of intensity equation (TIE) [11,12], optical diffraction tomography (ODT) [13,14], and so on, have been developed to help access to this valuable phase information.…”