Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain

Autor:	Chung, Hyungjin, Huh, Jaeyoung, Kim, Geon, Park, Yong Keun, Ye, Jong Chul
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: This will appear in IEEE Trans. on Computational Imaging
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.09022 Zobrazit plný text záznamu View this record from Arxiv