,Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field

• 作 者: Yichen WU Yilin Luo Gunvant Chaudhari Yair Rivenson Ayfer Calis Kevin de Haan Aydogan Ozcan
• 机 构: Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
• 出 处: 光：科学与应用（英文版）
• 发表日期: 2019年2期

Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram.However,unlike a conventional bright-field microscopy image,the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects.Here,we demonstrate that cross-modality deep leaing using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast,combining the volumetric imaging capability of holography with the speckle-and artifact-free image contrast of incoherent bright-field microscopy.We illustrate the performance of this "bright-field holography" method through the snapshot imaging of bioaerosols distributed in 3D,matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope.This data-driven deep-leaing-based imaging method bridges the contrast gap between coherent and incoherent imaging,and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram,benefiting from the wave-propagation framework of holography.