10 research outputs found
Holographic particle localization under multiple scattering
We introduce a novel framework that incorporates multiple scattering for
large-scale 3D particle-localization using single-shot in-line holography.
Traditional holographic techniques rely on single-scattering models which
become inaccurate under high particle-density. We demonstrate that by
exploiting multiple-scattering, localization is significantly improved. Both
forward and back-scattering are computed by our method under a tractable
recursive framework, in which each recursion estimates the next higher-order
field within the volume. The inverse scattering is presented as a nonlinear
optimization that promotes sparsity, and can be implemented efficiently. We
experimentally reconstruct 100 million object voxels from a single 1-megapixel
hologram. Our work promises utilization of multiple scattering for versatile
large-scale applications
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging
systems that reconstruct targets using compressed sensing, a technique for
solving underdetermined imaging problems when the target object exhibits
sparsity (i.e. the object can be described by a small number of non-zero
values, but the positions of these values are unknown). We nest an iterative,
unapproximated compressed sensing reconstruction algorithm into our end-to-end
optimization pipeline, resulting in an interpretable, data-efficient method for
maximally leveraging metaoptics to exploit object sparsity. We apply our
framework to super-resolution imaging and high-resolution depth imaging with a
phase-change material: in both situations, our end-to-end framework
computationally discovers optimal metasurface structures for compressed sensing
recovery, automatically balancing a number of complicated design
considerations. The optimized metasurface imaging systems are robust to noise,
significantly improving over random scattering surfaces and approaching the
ideal compressed sensing performance of a Gaussian matrix, showing how a
physical metasurface system can demonstrably approach the mathematical limits
of compressed sensing
Online Regularization by Denoising with Applications to Phase Retrieval
Regularization by denoising (RED) is a powerful framework for solving imaging
inverse problems. Most RED algorithms are iterative batch procedures, which
limits their applicability to very large datasets. In this paper, we address
this limitation by introducing a novel online RED (On-RED) algorithm, which
processes a small subset of the data at a time. We establish the theoretical
convergence of On-RED in convex settings and empirically discuss its
effectiveness in non-convex ones by illustrating its applicability to phase
retrieval. Our results suggest that On-RED is an effective alternative to the
traditional RED algorithms when dealing with large datasets.Comment: Accepted ICCVW 2019 (LCI