448 research outputs found
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from
its intensity measurements. As exemplified from quantitative phase imaging and
coherent diffraction imaging to adaptive optics, PR is essential for
reconstructing the refractive index distribution or topography of an object and
correcting the aberration of an imaging system. In recent years, deep learning
(DL), often implemented through deep neural networks, has provided
unprecedented support for computational imaging, leading to more efficient
solutions for various PR problems. In this review, we first briefly introduce
conventional methods for PR. Then, we review how DL provides support for PR
from the following three stages, namely, pre-processing, in-processing, and
post-processing. We also review how DL is used in phase image processing.
Finally, we summarize the work in DL for PR and outlook on how to better use DL
to improve the reliability and efficiency in PR. Furthermore, we present a
live-updating resource (https://github.com/kqwang/phase-recovery) for readers
to learn more about PR.Comment: 82 pages, 32 figure
High-throughput screening of encapsulated islets using wide-field lens-free on-chip imaging
Islet microencapsulation is a promising solution to diabetes treatment, but
its quality control based on manual microscopic inspection is extremely
low-throughput, highly variable and laborious. This study presents a
high-throughput islet-encapsulation quality screening system based on lens-free
on-chip imaging with a wide field-of-view of 18.15 cm^2, which is more than 100
times larger than that of a lens-based optical microscope, enabling it to image
and analyze ~8,000 microcapsules in a single frame. Custom-written image
reconstruction and processing software provides the user with clinically
important information, such as microcapsule count, size, intactness, and
information on whether each capsule contains an islet. This high-throughput and
cost-effective platform can be useful for researchers to develop better
encapsulation protocols as well as perform quality control prior to
transplantation
High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network
Quantitative phase microscopy (QPM) is a label-free technique that enables to
monitor morphological changes at subcellular level. The performance of the QPM
system in terms of spatial sensitivity and resolution depends on the coherence
properties of the light source and the numerical aperture (NA) of objective
lenses. Here, we propose high space-bandwidth QPM using partially spatially
coherent optical coherence microscopy (PSC-OCM) assisted with deep neural
network. The PSC source synthesized to improve the spatial sensitivity of the
reconstructed phase map from the interferometric images. Further, compatible
generative adversarial network (GAN) is used and trained with paired
low-resolution (LR) and high-resolution (HR) datasets acquired from PSC-OCM
system. The training of the network is performed on two different types of
samples i.e. mostly homogenous human red blood cells (RBC) and on highly
heterogenous macrophages. The performance is evaluated by predicting the HR
images from the datasets captured with low NA lens and compared with the actual
HR phase images. An improvement of 9 times in space-bandwidth product is
demonstrated for both RBC and macrophages datasets. We believe that the
PSC-OCM+GAN approach would be applicable in single-shot label free tissue
imaging, disease classification and other high-resolution tomography
applications by utilizing the longitudinal spatial coherence properties of the
light source
Roadmap on digital holography [Invited]
This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography
Single-shot experimental-numerical twin-image removal in lensless digital holographic microscopy
Lensless digital holographic microscopy (LDHM) offers very large
field-of-view label-free imaging crucial, e.g., in high-throughput particle
tracking and biomedical examination of cells and tissues. Compact layouts
promote point-of-case and out-of-laboratory applications. The LDHM, based on
the Gabor in-line holographic principle, is inherently spoiled by the
twin-image effect, which complicates the quantitative analysis of reconstructed
phase and amplitude maps. Popular family of solutions consists of numerical
methods, which tend to minimize twin-image upon iterative process based on data
redundancy. Additional hologram recordings are needed, and final results
heavily depend on the algorithmic parameters, however. In this contribution we
present a novel single-shot experimental-numerical twin-image removal technique
for LDHM. It leverages two-source off-axis hologram recording deploying simple
fiber splitter. Additionally, we introduce a novel phase retrieval numerical
algorithm specifically tailored to the acquired holograms, that provides
twin-image-free reconstruction without compromising the resolution. We
quantitatively and qualitatively verify proposed method employing phase test
target and cheek cells biosample. The results demonstrate that the proposed
technique enables low-cost, out-of-laboratory LDHM imaging with enhanced
precision, achieved through the elimination of twin-image errors. This
advancement opens new avenues for more accurate technical and biomedical
imaging applications using LDHM, particularly in scenarios where cost-effective
and portable imaging solutions are desired
Roadmap on holography
From its inception holography has proven an extremely productive and attractive area of research. While specific technical applications give rise to 'hot topics', and three-dimensional (3D) visualisation comes in and out of fashion, the core principals involved continue to lead to exciting innovations in a wide range of areas. We humbly submit that it is impossible, in any journal document of this type, to fully reflect current and potential activity; however, our valiant contributors have produced a series of documents that go no small way to neatly capture progress across a wide range of core activities. As editors we have attempted to spread our net wide in order to illustrate the breadth of international activity. In relation to this we believe we have been at least partially successful.This work was supported by Ministerio de EconomĂa, Industria y Competitividad (Spain) under projects FIS2017-82919-R (MINECO/AEI/FEDER, UE) and FIS2015-66570-P (MINECO/FEDER), and by Generalitat Valenciana (Spain) under project PROMETEO II/2015/015
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