502 research outputs found
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
Ensemble learning of diffractive optical networks
A plethora of research advances have emerged in the fields of optics and
photonics that benefit from harnessing the power of machine learning.
Specifically, there has been a revival of interest in optical computing
hardware, due to its potential advantages for machine learning tasks in terms
of parallelization, power efficiency and computation speed. Diffractive Deep
Neural Networks (D2NNs) form such an optical computing framework, which
benefits from deep learning-based design of successive diffractive layers to
all-optically process information as the input light diffracts through these
passive layers. D2NNs have demonstrated success in various tasks, including
e.g., object classification, spectral-encoding of information, optical pulse
shaping and imaging, among others. Here, we significantly improve the inference
performance of diffractive optical networks using feature engineering and
ensemble learning. After independently training a total of 1252 D2NNs that were
diversely engineered with a variety of passive input filters, we applied a
pruning algorithm to select an optimized ensemble of D2NNs that collectively
improve their image classification accuracy. Through this pruning, we
numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind
testing accuracies of 61.14% and 62.13%, respectively, on the classification of
CIFAR-10 test images, providing an inference improvement of >16% compared to
the average performance of the individual D2NNs within each ensemble. These
results constitute the highest inference accuracies achieved to date by any
diffractive optical neural network design on the same dataset and might provide
a significant leapfrog to extend the application space of diffractive optical
image classification and machine vision systems.Comment: 22 Pages, 4 Figures, 1 Tabl
Rapid Sensing of Hidden Objects and Defects using a Single-Pixel Diffractive Terahertz Processor
Terahertz waves offer numerous advantages for the nondestructive detection of
hidden objects/defects in materials, as they can penetrate through most
optically-opaque materials. However, existing terahertz inspection systems are
restricted in their throughput and accuracy (especially for detecting small
features) due to their limited speed and resolution. Furthermore, machine
vision-based continuous sensing systems that use large-pixel-count imaging are
generally bottlenecked due to their digital storage, data transmission and
image processing requirements. Here, we report a diffractive processor that
rapidly detects hidden defects/objects within a target sample using a
single-pixel spectroscopic terahertz detector, without scanning the sample or
forming/processing its image. This terahertz processor consists of passive
diffractive layers that are optimized using deep learning to modify the
spectrum of the terahertz radiation according to the absence/presence of hidden
structures or defects. After its fabrication, the resulting diffractive
processor all-optically probes the structural information of the sample volume
and outputs a spectrum that directly indicates the presence or absence of
hidden structures, not visible from outside. As a proof-of-concept, we trained
a diffractive terahertz processor to sense hidden defects (including
subwavelength features) inside test samples, and evaluated its performance by
analyzing the detection sensitivity as a function of the size and position of
the unknown defects. We validated its feasibility using a single-pixel
terahertz time-domain spectroscopy setup and 3D-printed diffractive layers,
successfully detecting hidden defects using pulsed terahertz illumination. This
technique will be valuable for various applications, e.g., security screening,
biomedical sensing, quality control, anti-counterfeiting measures and cultural
heritage protection.Comment: 23 Pages, 5 Figure
Learning Wavefront Coding for Extended Depth of Field Imaging
Depth of field is an important factor of imaging systems that highly affects
the quality of the acquired spatial information. Extended depth of field (EDoF)
imaging is a challenging ill-posed problem and has been extensively addressed
in the literature. We propose a computational imaging approach for EDoF, where
we employ wavefront coding via a diffractive optical element (DOE) and we
achieve deblurring through a convolutional neural network. Thanks to the
end-to-end differentiable modeling of optical image formation and computational
post-processing, we jointly optimize the optical design, i.e., DOE, and the
deblurring through standard gradient descent methods. Based on the properties
of the underlying refractive lens and the desired EDoF range, we provide an
analytical expression for the search space of the DOE, which is instrumental in
the convergence of the end-to-end network. We achieve superior EDoF imaging
performance compared to the state of the art, where we demonstrate results with
minimal artifacts in various scenarios, including deep 3D scenes and broadband
imaging
Aperture Diffraction for Compact Snapshot Spectral Imaging
We demonstrate a compact, cost-effective snapshot spectral imaging system
named Aperture Diffraction Imaging Spectrometer (ADIS), which consists only of
an imaging lens with an ultra-thin orthogonal aperture mask and a mosaic filter
sensor, requiring no additional physical footprint compared to common RGB
cameras. Then we introduce a new optical design that each point in the object
space is multiplexed to discrete encoding locations on the mosaic filter sensor
by diffraction-based spatial-spectral projection engineering generated from the
orthogonal mask. The orthogonal projection is uniformly accepted to obtain a
weakly calibration-dependent data form to enhance modulation robustness.
Meanwhile, the Cascade Shift-Shuffle Spectral Transformer (CSST) with strong
perception of the diffraction degeneration is designed to solve a
sparsity-constrained inverse problem, realizing the volume reconstruction from
2D measurements with Large amount of aliasing. Our system is evaluated by
elaborating the imaging optical theory and reconstruction algorithm with
demonstrating the experimental imaging under a single exposure. Ultimately, we
achieve the sub-super-pixel spatial resolution and high spectral resolution
imaging. The code will be available at: https://github.com/Krito-ex/CSST.Comment: accepted by International Conference on Computer Vision (ICCV) 202
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