Total variation-based reconstruction and phase retrieval for diffraction tomography

TL;DR

This paper introduces a novel method for optical diffraction tomography that reconstructs 3D images from intensity-only measurements using total variation regularization and a hybrid input-output scheme, achieving results comparable to phase-aware methods.

Contribution

The paper presents a new reconstruction algorithm for ODT that handles phaseless data, combining Fourier diffraction modeling, total variation regularization, and a hybrid input-output scheme.

Findings

Numerical results show successful 3D reconstructions from intensity-only data.

Reconstructed images are comparable to those obtained with phase information.

The method stabilizes the inversion process using total variation regularization.

Abstract

In optical diffraction tomography (ODT), the three-dimensional scattering potential of a microscopic object rotating around its center is recovered by a series of illuminations with coherent light. Reconstruction algorithms such as the filtered backpropagation require knowledge of the complex-valued wave at the measurement plane, whereas often only intensities, i.e., phaseless measurements, are available in practice. We propose a new reconstruction approach for ODT with unknown phase information based on three key ingredients. First, the light propagation is modeled using Born's approximation enabling us to use the Fourier diffraction theorem. Second, we stabilize the inversion of the non-uniform discrete Fourier transform via total variation regularization utilizing a primal-dual iteration, which also yields a novel numerical inversion formula for ODT with known phase. The third ingredient is a hybrid input-output scheme. We achieved convincing numerical results, which indicate that ODT with phaseless data is possible. The so-obtained 2D and 3D reconstructions are even comparable to the ones with known phase.