4,178 research outputs found
Phase Retrieval with Application to Optical Imaging
This review article provides a contemporary overview of phase retrieval in
optical imaging, linking the relevant optical physics to the information
processing methods and algorithms. Its purpose is to describe the current state
of the art in this area, identify challenges, and suggest vision and areas
where signal processing methods can have a large impact on optical imaging and
on the world of imaging at large, with applications in a variety of fields
ranging from biology and chemistry to physics and engineering
Sparsity-regularized coded ptychography for robust and efficient lensless microscopy on a chip
In ptychographic imaging, the trade-off between the number of acquisitions
and the resultant imaging quality presents a complex optimization problem.
Increasing the number of acquisitions typically yields reconstructions with
higher spatial resolution and finer details. Conversely, a reduction in
measurement frequency often compromises the quality of the reconstructed
images, manifesting as increased noise and coarser details. To address this
challenge, we employ sparsity priors to reformulate the ptychographic
reconstruction task as a total variation regularized optimization problem. We
introduce a new computational framework, termed the ptychographic proximal
total-variation (PPTV) solver, designed to integrate into existing ptychography
settings without necessitating hardware modifications. Through comprehensive
numerical simulations, we validate that PPTV-driven coded ptychography is
capable of producing highly accurate reconstructions with a minimal set of
eight intensity measurements. Convergence analysis further substantiates the
robustness, stability, and computational feasibility of the proposed PPTV
algorithm. Experimental results obtained from optical setups unequivocally
demonstrate that the PPTV algorithm facilitates high-throughput,
high-resolution imaging while significantly reducing the measurement burden.
These findings indicate that the PPTV algorithm has the potential to
substantially mitigate the resource-intensive requirements traditionally
associated with high-quality ptychographic imaging, thereby offering a pathway
toward the development of more compact and efficient ptychographic microscopy
systems.Comment: 15 pages, 7 figure
Blind Ptychographic Phase Retrieval via Convergent Alternating Direction Method of Multipliers
Ptychography has risen as a reference X-ray imaging technique: it achieves
resolutions of one billionth of a meter, macroscopic field of view, or the
capability to retrieve chemical or magnetic contrast, among other features. A
ptychographyic reconstruction is normally formulated as a blind phase retrieval
problem, where both the image (sample) and the probe (illumination) have to be
recovered from phaseless measured data. In this article we address a nonlinear
least squares model for the blind ptychography problem with constraints on the
image and the probe by maximum likelihood estimation of the Poisson noise
model. We formulate a variant model that incorporates the information of
phaseless measurements of the probe to eliminate possible artifacts. Next, we
propose a generalized alternating direction method of multipliers designed for
the proposed nonconvex models with convergence guarantee under mild conditions,
where their subproblems can be solved by fast element-wise operations.
Numerically, the proposed algorithm outperforms state-of-the-art algorithms in
both speed and image quality.Comment: 23 page
Roadmap on optical security
Postprint (author's final draft
PDEs with Compressed Solutions
Sparsity plays a central role in recent developments in signal processing,
linear algebra, statistics, optimization, and other fields. In these
developments, sparsity is promoted through the addition of an norm (or
related quantity) as a constraint or penalty in a variational principle. We
apply this approach to partial differential equations that come from a
variational quantity, either by minimization (to obtain an elliptic PDE) or by
gradient flow (to obtain a parabolic PDE). Also, we show that some PDEs can be
rewritten in an form, such as the divisible sandpile problem and
signum-Gordon. Addition of an term in the variational principle leads to
a modified PDE where a subgradient term appears. It is known that modified PDEs
of this form will often have solutions with compact support, which corresponds
to the discrete solution being sparse. We show that this is advantageous
numerically through the use of efficient algorithms for solving based
problems.Comment: 21 pages, 15 figure
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