7,370 research outputs found
Structured illumination microscopy with unknown patterns and a statistical prior
Structured illumination microscopy (SIM) improves resolution by
down-modulating high-frequency information of an object to fit within the
passband of the optical system. Generally, the reconstruction process requires
prior knowledge of the illumination patterns, which implies a well-calibrated
and aberration-free system. Here, we propose a new \textit{algorithmic
self-calibration} strategy for SIM that does not need to know the exact
patterns {\it a priori}, but only their covariance. The algorithm, termed
PE-SIMS, includes a Pattern-Estimation (PE) step requiring the uniformity of
the sum of the illumination patterns and a SIM reconstruction procedure using a
Statistical prior (SIMS). Additionally, we perform a pixel reassignment process
(SIMS-PR) to enhance the reconstruction quality. We achieve 2 better
resolution than a conventional widefield microscope, while remaining
insensitive to aberration-induced pattern distortion and robust against
parameter tuning
2D and 3D structured illumination microscopy with unknown patterns and a statistical prior
Structured illumination microscopy (SIM) is one of the most widely applied super-resolution microscopy techniques in bioimaging. It improves resolution by down-modulating a sample’s high spatial frequency information to fit within the passband of the optical system. Normally, the reconstruction process requires prior knowledge of the illumination patterns. Aberrations from the optical system or from the sample itself will distort the patterns and degrade performance. Here, we propose a new algorithmic self-calibration strategy for both 2D and 3D SIM that does not need to know the exact patterns a priori, but only their covariance. The algorithm, termed PE-SIMS, includes a pattern-estimation (PE) step requiring the uniformity of the sum of the illumination patterns and a SIM reconstruction procedure using a statistical prior (SIMS). We achieve 2x better resolution than a conventional widefield microscope, without needing to know the illumination patterns and while remaining insensitive to aberration-induced pattern distortion.
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Untrained, physics-informed neural networks for structured illumination microscopy
In recent years there has been great interest in using deep neural networks
(DNN) for super-resolution image reconstruction including for structured
illumination microscopy (SIM). While these methods have shown very promising
results, they all rely on data-driven, supervised training strategies that need
a large number of ground truth images, which is experimentally difficult to
realize. For SIM imaging, there exists a need for a flexible, general, and
open-source reconstruction method that can be readily adapted to different
forms of structured illumination. We demonstrate that we can combine a deep
neural network with the forward model of the structured illumination process to
reconstruct sub-diffraction images without training data. The resulting
physics-informed neural network (PINN) can be optimized on a single set of
diffraction limited sub-images and thus doesn't require any training set. We
show with simulated and experimental data that this PINN can be applied to a
wide variety of SIM methods by simply changing the known illumination patterns
used in the loss function and can achieve resolution improvements that match
well with theoretical expectations.Comment: Preprint for journal submission. 21 Pages. 5 main text figures. 6
supplementary figure
Direct 3D Tomographic Reconstruction and Phase-Retrieval of Far-Field Coherent Diffraction Patterns
We present an alternative numerical reconstruction algorithm for direct
tomographic reconstruction of a sample refractive indices from the measured
intensities of its far-field coherent diffraction patterns. We formulate the
well-known phase-retrieval problem in ptychography in a tomographic framework
which allows for simultaneous reconstruction of the illumination function and
the sample refractive indices in three dimensions. Our iterative reconstruction
algorithm is based on the Levenberg-Marquardt algorithm. We demonstrate the
performance of our proposed method with simulation studies
Machine learning -- based diffractive imaging with subwavelength resolution
Far-field characterization of small objects is severely constrained by the
diffraction limit. Existing tools achieving sub-diffraction resolution often
utilize point-by-point image reconstruction via scanning or labelling. Here, we
present a new imaging technique capable of fast and accurate characterization
of two-dimensional structures with at least wavelength/25 resolution, based on
a single far-field intensity measurement. Experimentally, we realized this
technique resolving the smallest-available to us 180-nm-scale features with
532-nm laser light. A comprehensive analysis of machine learning algorithms was
performed to gain insight into the learning process and to understand the flow
of subwavelength information through the system. Image parameterization,
suitable for diffractive configurations and highly tolerant to random noise was
developed. The proposed technique can be applied to new characterization tools
with high spatial resolution, fast data acquisition, and artificial
intelligence, such as high-speed nanoscale metrology and quality control, and
can be further developed to high-resolution spectroscop
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