Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

• 来源期刊: 光：科学与应用（英文版）
• 年，卷（期）: 2019,（2）
• 页码范围: 219-225
• 页数: 7页
• 语种: 英文

摘要

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 learning 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-learning-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.