Holographic particle localization under multiple scattering

Abstract

We introduce a novel framework that incorporates multiple scattering for large-scale 3D particle-localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models which become inaccurate under high particle-density. We demonstrate that by exploiting multiple-scattering, localization is significantly improved. Both forward and back-scattering are computed by our method under a tractable recursive framework, in which each recursion estimates the next higher-order field within the volume. The inverse scattering is presented as a nonlinear optimization that promotes sparsity, and can be implemented efficiently. We experimentally reconstruct 100 million object voxels from a single 1-megapixel hologram. Our work promises utilization of multiple scattering for versatile large-scale applications.

References in corpus (6)

• Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media
• Efficient and accurate inversion of multiple scattering with deep learning
• High-throughput intensity diffraction tomography with a computational microscope
• A convergent Born series for solving the inhomogeneous Helmholtz equation in arbitrarily large media
• A Recursive Born Approach to Nonlinear Inverse Scattering
• Assessment of learning tomography using Mie theory

Cited by in corpus (5)

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• Role of Multiple Scattering in Single Particle Perturbations in Absorbing Random Media
• Unified treatment of exact and approximate scalar electromagnetic wave scattering