Array imaging using intensity-only measurements

By Anwei Chai, Miguel Moscoso, George Papanicolaou. 2010

201 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/46673a9b1fd22685dc8f7f38e65b3bbc42470f6c

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In Biomedical Engineering

In Computational Mechanics

In Mechanics of Materials

Citations in the corpus

Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow (2016, background): “The plethora of applications include X-ray crystallo graphy [12], [8], optics [13], [14], as well as array and high-power coherent diffractive imaging [15], [1 6], [17], to astronomy [18], and microscopy [19], where due to physical limitations, optical sensors/detect ors such as charge-coupled devic”
Phase Retrieval via Matrix Completion (2011, methodology): “In terms of framework, the closest approach is the paper [18], in which the authors use a matrix completion approach for array imaging from intensity measurements.”
Phase recovery, MaxCut and complex semidefinite programming (2012, methodology): “While the resulting SDP is typically larger than the PhaseLift relaxation, its simple structure (the constraint matrices are singletons) allows us to solve it very efﬁciently.”
Phase Retrieval from Coded Diffraction Patterns (2013, unknown): “In addition to X-ray crystallography [33, 39], the phase problem has numerous other applications in the imaging sciences such as diﬀraction and array imaging [18, 24], optics [54], speckle imaging in astronomy [26], and microscopy [38].”
PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming (2011, background): “Although this is a fairly standard relaxation in control [4,18], the idea of casting the phase retrieval problem as a trace-minimization problem over an affine slice of the positive semidefinite cone is very recent [7,10].”
Phase Retrieval via Wirtinger Flow: Theory and Algorithms (2014, methodology): “Because of the practical significance of the phase retrieval problem in imaging science, the community has developed methods for recovering a signal x ∈ Cn from data of the form yr = ∣⟨ar,x⟩∣2 in the special case where one samples the (square) modulus of its Fourier transform.”
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage (2017, background): “Let us outline a convex approach [3, 8, 10, 21] to the phase retrieval problem.”
Phase Retrieval Using Alternating Minimization (2013, methodology): “Our contribution : • The iterative part of our algorithm is implicit in previous work [17, 15, 43, 5]; the novelty in our algorithmic contribution is the initialization step which makes it more likely for the iterative procedure to succeed - see Figures 1 and 2.”
A Geometric Analysis of Phase Retrieval (2016, background): “GPR also sees applications in electron microscopy [71], diffraction and array imaging [26,34], acoustics [15,16], quantum mechanics [38,86] and quantum information [54].”
Compressive phase retrieval via generalized approximate message passing (2012, background): “Moreover, PhaseLift's recovery was shown [14] to be robust to additive noise on the squared magnitudes y 2 i .”