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Phase Retrieval Under a Generative Prior [article]

Paul Hand, Oscar Leong, Vladislav Voroninski
2018 arXiv   pre-print
In this paper, we propose a novel framework for phase retrieval by 1) modeling natural signals as being in the range of a deep generative neural network G : R^k →R^n and 2) enforcing this prior directly  ...  We corroborate these results with experiments showing that exploiting generative models in phase retrieval tasks outperforms sparse phase retrieval methods.  ...  Hence we study the phase retrieval problem under a generative prior : find x ∈ R k such that b = |AG(x)|. We will refer to this formulation as Deep Phase Retrieval (DPR).  ... 
arXiv:1807.04261v1 fatcat:r7nkdt3b6vcxnnezktsrynjgo4

Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior [article]

Fahad Shamshad, Ali Ahmed
2020 arXiv   pre-print
We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors; one is trained on sharp images and the other on blur kernels  ...  The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that best explain the forward measurement model.  ...  One other Algorithm 1 Phaseless Deblurring under Generative Priors Input: y, G I , G K , and A Output: Estimatesî andk Initialize: z (0) i ∼ N(0, 1), z (0) k ∼ N(0, 1) for t = 0, 1, 2, . . . , T do z  ... 
arXiv:2002.12578v1 fatcat:p3ioca5zxzfe3o5zdfskc3thvy

Exact asymptotics for phase retrieval and compressed sensing with random generative priors [article]

Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová
2020 arXiv   pre-print
In particular, while sparsity does not allow to perform compressive phase retrieval efficiently close to its information-theoretic limit, it is found that under the random generative prior compressed phase  ...  We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix.  ...  Acknowledgements This work is supported by the ERC under the European Union's Horizon 2020 Research and Innovation Program 714608-SMiLe, as well as by the French Agence Nationale de la Recherche under  ... 
arXiv:1912.02008v2 fatcat:m5u7cq6zpbgcln432gsk3kyjbq

Phase retrieval for Fourier Ptychography under varying amount of measurements [article]

Lokesh Boominathan, Mayug Maniparambil, Honey Gupta, Rahul Baburajan, Kaushik Mitra
2018 arXiv   pre-print
At the crux of this method is the phase retrieval algorithm, which is used for computationally stitching together low-resolution images taken under varying illumination angles of a coherent light source  ...  We show that an auto-encoder based architecture can be adaptively trained for phase retrieval under both low overlap, where traditional techniques completely fail, and at higher levels of overlap.  ...  7 Supplementary material for Phase retrieval for Fourier Ptychography under varying amount of measurements Generator trained with Forward model loss on simulated data Intensity Phase 65% overlap  ... 
arXiv:1805.03593v1 fatcat:2tw2x7sszzeu7gkiiic4hczsaa

Phase Retrieval via Iterative Hard Thresholding Backtracking Algorithm

Li-Juan ZHU, Jian-Jun WANG
2016 DEStech Transactions on Engineering and Technology Research  
In this paper, we study the problem of phase retrieval via an improved iterative hard thresholding (IHT) algorithm, which is called the backtracking-based iterative hard thresholding (BIHT) algorithm.  ...  The IHT algorithm should start with a careful initialization, but the BIHT does not need to meet this requirement.  ...  Fig.1(a) shows the IHT algorithm in a general initial value and without any prior information.  ... 
doi:10.12783/dtetr/ssme-ist2016/4003 fatcat:pttmcn7fsfdsdgdv5ivd6ope7m

The testing effect under divided attention: Educational application

Zachary L. Buchin, Neil W. Mulligan
2019 Journal of experimental psychology. Applied  
Participants studied foreign language word pairs (Experiments 1 and 2) or educational texts (Experiment 3), restudied or retrieved those materials under full attention (FA) or DA, and then took a final  ...  In each experiment, a testing effect was found under both FA and DA and the level of disruption from DA was similar for both learning conditions.  ...  In general, both experiments found a similar pattern in final memory performance in that the word-pairs retrieved under DA during phase 2 were much less affected than word-pairs restudied under DA during  ... 
doi:10.1037/xap0000230 pmid:31070392 fatcat:pirbmlw22vb5xchpgwsfyendly

A New Optical Surface Measurement Method with Iterative Sparsity-Constrained Threshold Phase Retrieval Algorithm

Yi Niu, Yang Liu, Guangming Shi, Dahua Gao, Guo Li
2014 The Scientific World Journal  
In addition, to further improve the performance, we propose a new iterative threshold phase retrieval algorithm with sparsity-constraint to effectively reconstruct the phase of wave field.  ...  Due to its low complexity and acceptable accuracy, phase retrieval technique has been proposed as an alternative to solve the classic optical surface measurement task.  ...  In addition, we incorporate the sparsity prior into the traditional phase retrieval techniques which provides a more efficient way to faithfully reconstruct the phase of the under test surfaces.  ... 
doi:10.1155/2014/548395 pmid:25161394 pmcid:PMC4000659 fatcat:2wufonb5ifccnevble5bjehh3q

Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval [article]

Rakib Hyder, Viraj Shah, Chinmay Hegde, M. Salman Asif
2019 arXiv   pre-print
We empirically show that the performance of our method with projected gradient descent is superior to the existing approach for solving phase retrieval under generative priors.  ...  In this work, we propose replacing the sparsity/support priors with generative priors and propose two algorithms to solve the phase retrieval problem.  ...  [6, 7] solves the phase retrieval problem using generative priors through enforcing the prior directly by minimizing an empirical risk objective over the domain of the generator.  ... 
arXiv:1903.02707v1 fatcat:bjurs2mez5djhcvz4vwxszirmq

Retrieval mode distinguishes the testing effect from the generation effect

Jeffrey D. Karpicke, Franklin M. Zaromb
2010 Journal of Memory and Language  
Intentional retrieval produces greater subsequent retention than generating targets under incidental retrieval instructions.  ...  A series of four experiments examined the effects of generation vs. retrieval practice on subsequent retention. Subjects were first exposed to a list of target words.  ...  Thus the subjects in all conditions experienced the target words under incidental learning conditions in an initial exposure phase prior to the read/generate/recall manipulation.  ... 
doi:10.1016/j.jml.2009.11.010 fatcat:ipnpuzr5hvh7jn2iqk6mjkgp34

Signal retrieval with measurement system knowledge using variational generative model [article]

Zheyuan Zhu, Yangyang Sun, Johnathon White, Zenghu Chang, Shuo Pang
2019 arXiv   pre-print
In this work we introduce a variational generative model to capture the distribution of all possible signals, given a particular measurement.  ...  By exploiting the known measurement model in the variational generative framework, our signal retrieval process resolves the ambiguity in the forward process, and learns to retrieve signals that satisfy  ...  From the Bayesian perspective, using a generative model approach to derive a more accurate prior distribution than Eq.(4) becomes a logical follow-up [23] - [25] .  ... 
arXiv:1909.04188v1 fatcat:sqiob7h2hvezppgf7q6ui5auzy

Compressive phase retrieval via generalized approximate message passing

Philip Schniter, Sundeep Rangan
2012 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton)  
Goal: Recover signal x 0 ∈ C n from m magnitude-only measurements where A ∈ C m×n is a known linear transform and w ∈ C m is noise.  ...  Motivation: In many applications, it feasible to measure the intensity, but not the phase, of the Fourier transform of the signal-of-interest:  ...  The Bayesian AMP [Donoho/Maleki/Montanari'10] extended the above to a generic i.i.d signal prior and MMSE estimation.  ... 
doi:10.1109/allerton.2012.6483302 dblp:conf/allerton/SchniterR12 fatcat:e232ufdrknadlktdkgiqjv73se

Robust Compressive Phase Retrieval via Deep Generative Priors [article]

Fahad Shamshad, Ali Ahmed
2018 arXiv   pre-print
This paper proposes a new framework to regularize the highly ill-posed and non-linear phase retrieval problem through deep generative priors using simple gradient descent algorithm.  ...  Finally, we show the effectiveness of the proposed approach on a real transmission matrix dataset in an actual application of multiple scattering media imaging.  ...  under Algorithm 1 Phase retrieval via generative prior Input: y, G and η Output: Estimatesx Initialize: z 0 := N (0, I K ) for t = 1, 2, 3, . . .  ... 
arXiv:1808.05854v1 fatcat:bu3lhhrbqvgc7lddonj6b6foqq

The Effect of Retrieval Cues on Visual Preferences and Memory in Infancy: Evidence for a Four-Phase Attention Function

Lorraine E. Bahrick, Maria Hernandez-Reif, Jeffrey N. Pickens
1997 Journal of Experimental Child Psychology  
The present study tested a hypothesis generated from this model that a retrieval cue would increase memory accessibility and shift visual preferences toward greater novelty to resemble more recent memories  ...  Further, a second experiment found that increasing the exposure to the retrieval cue could shift the familiarity preference to a novelty preference.  ...  This pattern suggest~ that enhancing memory with a: retrieval cue at a given phase reinstates preferences characteristic of a prior phase.  ... 
doi:10.1006/jecp.1997.2399 pmid:9344484 fatcat:jju55q5mbrbf3dmobkdmmdw6gm

Large-scale phase retrieval

Xuyang Chang, Liheng Bian, Jun Zhang
2021 eLight  
This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization.  ...  In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements.  ...  inapplicable for general large-scale phase retrieval.  ... 
doi:10.1186/s43593-021-00004-w fatcat:qezlwljfivd2vf4qt74fvdp7ma

Large-scale phase retrieval [article]

Xuyang Chang, Liheng Bian, Jun Zhang
2021 arXiv   pre-print
This framework compensates the shortcomings of each operator, so as to realize high-fidelity phase retrieval with low computational complexity and strong generalization.  ...  In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements.  ...  As a result, LPR enables generalized large-scale phase retrieval with high fidelity and low computational complexity, making it a state-of-the-art method for various computational phase imaging applications  ... 
arXiv:2104.03148v1 fatcat:fijsigvilbafvggc7xidc7r2g4
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