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Zero-Shot Learning of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements

2021-11-27 06:05:47
Renhao Liu, Yu Sun, Jiabei Zhu, Lei Tian, Ulugbek Kamilov
arXiv_CV
           
Abstract

Intensity diffraction tomography (IDT) refers to a class of optical microscopy techniques for imaging the 3D refractive index (RI) distribution of a sample from a set of 2D intensity-only measurements. The reconstruction of artifact-free RI maps is a fundamental challenge in IDT due to the loss of phase information and the missing cone problem. Neural fields (NF) has recently emerged as a new deep learning (DL) paradigm for learning continuous representations of complex 3D scenes without external training datasets. We present DeCAF as the first NF-based IDT method that can learn a high-quality continuous representation of a RI volume directly from its intensity-only and limited-angle measurements. We show on three different IDT modalities and multiple biological samples that DeCAF can generate high-contrast and artifact-free RI maps.

Abstract (translated)

URL

https://arxiv.org/abs/2112.00002

PDF

https://arxiv.org/pdf/2112.00002.pdf

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