Results 61 to 70 of about 7,807,536 (173)
Fast Phase Retrieval from Local Correlation Measurements
, 2016 We develop a fast phase retrieval method which can utilize a large class of local phaseless correlation-based measurements in order to recover a given signal ${\bf x} \in \mathbb{C}^d$ (up to an unknown global phase) in near-linear $\mathcal{O} \left( d \
Iwen, Mark +2 more
core +2 more sources
Phase retrieval and saddle-point optimization
, 2007 Iterative algorithms with feedback are amongst the most powerful and versatile optimization methods for phase retrieval. Among these, the hybrid input-output algorithm has demonstrated practical solutions to giga-element nonlinear phase retrieval ...
Bauschke +25 more
core +1 more source
Phase retrieval and norm retrieval
, 2014 Phase retrieval has become a very active area of research. We will classify when phase retrieval by Parseval frames passes to the Naimark complement and when phase retrieval by projections passes to the orthogonal complements. We introduce a new concept we call norm retrieval and show that this is what is necessary for passing phase retrieval to ...
Bahmanpour, Saeid +4 more
openaire +2 more sources
Phase retrieval from low-rate samples [PDF]
, 2014 The paper considers the phase retrieval problem in N-dimensional complex vector spaces. It provides two sets of deterministic measurement vectors which guarantee signal recovery for all signals, excluding only a specific subspace and a union of subspaces,
Boche, Holger, Pohl, Volker, Yang, Fanny
core
Low-complexity implementation of convex optimization-based phase retrieval
, 2018 Phase retrieval has important applications in optical imaging, communications and sensing. Lifting the dimensionality of the problem allows phase retrieval to be approximated as a convex optimization problem in a higher-dimensional space.
Arik, Sercan O., Kahn, Joseph M.
core +1 more source
Projections and phase retrieval
Applied and Computational Harmonic Analysis, 2017 We characterize collections of orthogonal projections for which it is possible to reconstruct a vector from the magnitudes of the corresponding projections. As a result we are able to show that in an $M$-dimensional real vector space a vector can be reconstructed from the magnitudes of its projections onto a generic collection of $N \geq 2M-1 ...
openaire +2 more sources
Benchmark Problems for Phase Retrieval [PDF]
SIAM Journal on Imaging Sciences, 2018 27 pages, 10 figures, new references, modified appendix, improved ...
Veit Elser, Ti-Yen Lan, Tamir Bendory
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Balancing Sparsity and Rank Constraints in Quadratic Basis Pursuit [PDF]
, 2014 We investigate the methods that simultaneously enforce sparsity and low-rank structure in a matrix as often employed for sparse phase retrieval problems or phase calibration problems in compressive sensing.
Bilen, Cagdas +3 more
core +4 more sources
, 2020 While characterization of coherent wavefields is essential to laser, x-ray and electron imaging, sensors measure the squared magnitude of the field, rather than the field itself. Holography or phase retrieval must be used to characterize the field. The need for a reference severely restricts the utility of holography.
Brady, David J. +2 more
openaire +2 more sources