Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography

• URL: http://arxiv.org/abs/2103.09022v1
• Date: Tue, 16 Mar 2021 12:41:33 GMT
• Title: Unsupervised Missing Cone Deep Learning in Optical Diffraction Tomography
• Authors: Hyungjin Chung, Jaeyoung Huh, Geon Kim, Yong Keun Park, Jong Chul Ye
• Abstract summary: We present a novel unsupervised deep learning framework, which learns the probability distribution of missing projection views through optimal transport driven cycleGAN. Experimental results show that missing cone artifact in ODT can be significantly resolved by the proposed method.
• Score: 25.18730153421617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical diffraction tomography (ODT) produces three dimensional distribution of refractive index (RI) by measuring scattering fields at various angles. Although the distribution of RI index is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along axial direction compared to the horizontal imaging plane. To solve this issue, here we present a novel unsupervised deep learning framework, which learns the probability distribution of missing projection views through optimal transport driven cycleGAN. Experimental results show that missing cone artifact in ODT can be significantly resolved by the proposed method.

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