1,163 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
Reference-less measurement of the transmission matrix of a highly scattering material using a DMD and phase retrieval techniques
This paper investigates experimental means of measuring the transmission
matrix (TM) of a highly scattering medium, with the simplest optical setup.
Spatial light modulation is performed by a digital micromirror device (DMD),
allowing high rates and high pixel counts but only binary amplitude modulation.
We used intensity measurement only, thus avoiding the need for a reference
beam. Therefore, the phase of the TM has to be estimated through signal
processing techniques of phase retrieval. Here, we compare four different phase
retrieval principles on noisy experimental data. We validate our estimations of
the TM on three criteria : quality of prediction, distribution of singular
values, and quality of focusing. Results indicate that Bayesian phase retrieval
algorithms with variational approaches provide a good tradeoff between the
computational complexity and the precision of the estimates
Three-dimensional imaging with multiple degrees of freedom using data fusion
This paper presents an overview of research work
and some novel strategies and results on using data fusion in
3-D imaging when using multiple information sources. We examine
a variety of approaches and applications such as 3-D
imaging integrated with polarimetric and multispectral imaging,
low levels of photon flux for photon-counting 3-D imaging,
and image fusion in both multiwavelength 3-D digital holography
and 3-D integral imaging. Results demonstrate the benefits
data fusion provides for different purposes, including visualization
enhancement under different conditions, and 3-D reconstruction
quality improvement
- …