KEYWORDS: Holograms, Holography, Microscopy, 3D modeling, Signal to noise ratio, 3D image reconstruction, Stereoscopy, Microscopes, Speckle, Time metrology
Holographic microscopy encodes the 3D information of a sample into a single hologram. However, holographic images are in general inferior to bright-field microscopy images in terms of contrast and signal-to-noise ratio, due to twin-image artifacts, speckle and out-of-plane interference. The contrast and noise problem of holography can be mitigated using iterative algorithms, but at the cost of additional measurements and time. Here, we present a deep-learning-based cross-modality imaging method to reconstruct a single hologram into volumetric images of a sample with bright-field contrast and SNR, merging the snapshot 3D imaging capability of holography with the image quality of bright-field microscopy.
KEYWORDS: Digital holography, Holography, Microscopy, 3D image reconstruction, Digital imaging, Holograms, Digital recording, Speckle, 3D image processing, Wave propagation interference
We demonstrate a deep learning-based hologram reconstruction method that achieves bright-field microscopy image contrast in digital holographic microscopy (DHM), which we termed as “bright-field holography”. In bright-field holography, a generative adversarial network was trained to transform a complex-valued DHM reconstruction (obtained without phase-retrieval) into an equivalent image captured by a high-NA bright-field microscope, corresponding to the same sample plane. As a proof-of-concept, we demonstrated snapshot imaging of pollen samples distributed in 3D, digitally matching the contrast and shallow depth-of-field advantages of bright-field microscopy; this enabled us to digitally image a sample volume using bright-field holography without any physical axial scanning.
There is an increasing but unmet need for accurate, label-free and automated bio-aerosol sensing. To address this need, we developed a high-throughput, cost-effective and portable bio-aerosol sensor based on computational microscopy and deep-learning. Our device is composed of an impactor and a lens-less digital holographic on-chip microscope. It screens air at 13 liters per minute, and captures bio-aerosols on the impactor substrate. An image sensor then records the in-line holograms of these captured bio-aerosols in real time. Using these recorded in-line holograms, the captured bio-aerosols are analyzed within a minute, facilitated by two deep convolutional neural networks (CNNs): the first CNN simultaneously performs auto-focusing and phase-recovery to reconstruct both the amplitude and phase images of each bio-aerosol with sub-micron resolution; and the second CNN performs automatic classification of the reconstructed bio-aerosols into pre-trained classes and counting their densities in air.
As a proof-of-concept, we demonstrated reconstruction and label-free sensing of five different types of bio-aerosols: Bermuda grass pollen, oak tree pollen, ragweed pollen, Aspergillus spore, and Alternaria spore. These bio-aerosols form some of the most common allergens in air. Using our mobile bio-aerosol sensor, we achieved ~94% precision and recall in differentiating these bio-aerosols without the use of any labeling. We also demonstrated successful sensing of oak tree pollens in the field using our mobile device. To the best of our knowledge, this is the first demonstration of automated label-free sensing of bio-aerosols using a portable device, which is enabled by computational microscopy and deep-learning. It has broad applications in label-free bio-aerosol sensing and air-quality monitoring.
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