449 research outputs found
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
Piston sensing for sparse aperture systems via all-optical diffractive neural network
It is a crucial issue to realize real-time piston correction in the area of
sparse aperture imaging. This paper introduces an optical diffractive neural
network-based piston sensing method, which can achieve light-speed sensing. By
using detectable intensity to represent pistons, the proposed method is capable
of converting complex amplitude distribution of the imaging optical field into
piston values directly. Differing from the electrical neural network, the way
of intensity representation enables the method to obtain the predicted pistons
without imaging acquisition and electrical processing process. The simulations
demonstrate the feasibility of the method for point source, and high accuracies
are achieved for both monochromatic light and broadband light. This method can
greatly improve the real-time performance of piston sensing and contribute to
the development of the sparse aperture system.Comment: 5 pages, 6 figure
Cascadable all-optical NAND gates using diffractive networks
Owing to its potential advantages such as scalability, low latency and power
efficiency, optical computing has seen rapid advances over the last decades. A
core unit of a potential all-optical processor would be the NAND gate, which
can be cascaded to perform an arbitrary logical operation. Here, we present the
design and analysis of cascadable all-optical NAND gates using diffractive
neural networks. We encoded the logical values at the input and output planes
of a diffractive NAND gate using the relative optical power of two
spatially-separated apertures. Based on this architecture, we numerically
optimized the design of a diffractive neural network composed of 4 passive
layers to all-optically perform NAND operation using the diffraction of light,
and cascaded these diffractive NAND gates to perform complex logical functions
by successively feeding the output of one diffractive NAND gate into another.
We demonstrated the cascadability of our diffractive NAND gates by using
identical diffractive designs to all-optically perform AND and OR operations,
as well as a half-adder. Cascadable all-optical NAND gates composed of
spatially-engineered passive diffractive layers can serve as a core component
of various optical computing platforms.Comment: 24 Pages, 5 Figure
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