Relevant bibliographies by topics / Compressive phase retrieval

Academic literature on the topic 'Compressive phase retrieval'

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Published: 6 September 2023

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Journal articles on the topic "Compressive phase retrieval"

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Li, Yi, and Vasileios Nakos. "Sublinear-Time Algorithms for Compressive Phase Retrieval." IEEE Transactions on Information Theory 66, no. 11 (November 2020): 7302–10. http://dx.doi.org/10.1109/tit.2020.3020701.

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Zhang, Liang, Gang Wang, Georgios B. Giannakis, and Jie Chen. "Compressive Phase Retrieval via Reweighted Amplitude Flow." IEEE Transactions on Signal Processing 66, no. 19 (October 1, 2018): 5029–40. http://dx.doi.org/10.1109/tsp.2018.2862395.

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Schniter, Philip, and Sundeep Rangan. "Compressive Phase Retrieval via Generalized Approximate Message Passing." IEEE Transactions on Signal Processing 63, no. 4 (February 2015): 1043–55. http://dx.doi.org/10.1109/tsp.2014.2386294.

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Peng, Tong, Runze Li, Junwei Min, Dan Dan, Meiling Zhou, Xianghua Yu, Chunmin Zhang, Chen Bai, and Baoli Yao. "Quantitative Phase Retrieval Through Scattering Medium via Compressive Sensing." IEEE Photonics Journal 14, no. 1 (February 2022): 1–8. http://dx.doi.org/10.1109/jphot.2021.3136509.

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Di, Hong, and Xin Zhang. "Compressive image encryption with customized key based on phase retrieval." Optical Engineering 56, no. 2 (February 10, 2017): 023103. http://dx.doi.org/10.1117/1.oe.56.2.023103.

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Jerez, Andres, Samuel Pinilla, and Henry Arguello. "Fast Target Detection via Template Matching in Compressive Phase Retrieval." IEEE Transactions on Computational Imaging 6 (2020): 934–44. http://dx.doi.org/10.1109/tci.2020.2995999.

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Ohlsson, Henrik, Allen Y. Yang, Roy Dong, and S. Shankar Sastry. "Compressive Phase Retrieval From Squared Output Measurements Via Semidefinite Programming*." IFAC Proceedings Volumes 45, no. 16 (July 2012): 89–94. http://dx.doi.org/10.3182/20120711-3-be-2027.00415.

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Li, Yingying, Jinchuan Zhou, Zhongfeng Sun, and Jingyong Tang. "Heavy-Ball-Based Hard Thresholding Pursuit for Sparse Phase Retrieval Problems." Mathematics 11, no. 12 (June 16, 2023): 2744. http://dx.doi.org/10.3390/math11122744.

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We introduce a novel iterative algorithm, termed the Heavy-Ball-Based Hard Thresholding Pursuit for sparse phase retrieval problem (SPR-HBHTP), to reconstruct a sparse signal from a small number of magnitude-only measurements. Our algorithm is obtained via a natural combination of the Hard Thresholding Pursuit for sparse phase retrieval (SPR-HTP) and the classical Heavy-Ball (HB) acceleration method. The robustness and convergence for the proposed algorithm were established with the help of the restricted isometry property. Furthermore, we prove that our algorithm can exactly recover a sparse signal with overwhelming probability in finite steps whenever the initialization is in the neighborhood of the underlying sparse signal, provided that the measurement is accurate. Extensive numerical tests show that SPR-HBHTP has a markedly improved recovery performance and runtime compared to existing alternatives, such as the Hard Thresholding Pursuit for sparse phase retrieval problem (SPR-HTP), the SPARse Truncated Amplitude Flow (SPARTA), and Compressive Phase Retrieval with Alternating Minimization (CoPRAM).
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Pedarsani, Ramtin, Dong Yin, Kangwook Lee, and Kannan Ramchandran. "PhaseCode: Fast and Efficient Compressive Phase Retrieval Based on Sparse-Graph Codes." IEEE Transactions on Information Theory 63, no. 6 (June 2017): 3663–91. http://dx.doi.org/10.1109/tit.2017.2693287.

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Hu, Chen, Xiaodong Wang, Linglong Dai, and Junjie Ma. "Partially Coherent Compressive Phase Retrieval for Millimeter-Wave Massive MIMO Channel Estimation." IEEE Transactions on Signal Processing 68 (2020): 1673–87. http://dx.doi.org/10.1109/tsp.2020.2975914.

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Dissertations / Theses on the topic "Compressive phase retrieval"

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Tian, Lei Ph D. Massachusetts Institute of Technology. "Compressive phase retrieval." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81756.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 129-138).
Recovering a full description of a wave from limited intensity measurements remains a central problem in optics. Optical waves oscillate too fast for detectors to measure anything but time{averaged intensities. This is unfortunate since the phase can reveal important information about the object. When the light is partially coherent, a complete description of the phase requires knowledge about the statistical correlations for each pair of points in space. Recovery of the correlation function is a much more challenging problem since the number of pairs grows much more rapidly than the number of points. In this thesis, quantitative phase imaging techniques that works for partially coherent illuminations are investigated. In order to recover the phase information with few measurements, the sparsity in each underly problem and ecient inversion methods are explored under the framework of compressed sensing. In each phase retrieval technique under study, diffraction during spatial propagation is exploited as an effective and convenient mechanism to uniformly distribute the information about the unknown signal into the measurement space. Holography is useful to record the scattered field from a sparse distribution of particles; the ability of localizing each particles using compressive reconstruction method is studied. When a thin sample is illuminated with partially coherent waves, the transport of intensity phase retrieval method is shown to be eective to recover the optical path length of the sample and remove the eect of the illumination. This technique is particularly suitable for X-ray phase imaging since it does not require a coherent source or any optical components. Compressive tomographic reconstruction, which makes full use of the priors that the sample consists of piecewise constant refractive indices, are demonstrated to make up missing data. The third technique, known as the phase space tomography (PST), addresses the correlation function recovery problem. Implementing the PST involves measuring many intensity images under spatial propagation. Experimental demonstration of a compressive reconstruction method, which finds the sparse solution by decomposing the correlation function into a few mutually uncorrelated coherent modes, is presented to produce accurate reconstruction even when the measurement suers from the 'missing cone' problem in the Fourier domain.
by Lei Tian.
Ph.D.
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Saqueb, Syed An Nazmus. "Computational THz Imaging: High-resolution THz Imaging via Compressive Sensing and Phase-retrieval Algorithms." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1545836443000865.

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Killedar, Vinayak. "Solving Inverse Problems Using a Deep Generative Prior." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5234.

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In an inverse problem, the objective is to recover a signal from its measurements, given the knowledge of the measurement operator. In this thesis, we address the problems of compressive sensing (CS) and compressive phase retrieval (CPR) using a generative prior model with sparse latent sampling. These problems are ill-posed and have infinite solutions. Structural assumptions such as smoothness, sparsity and non-negativity are imposed on the solution to obtain a unique and meaningful solution. The standard CS and CPR formulations impose a sparsity prior on the signal. Recently, generative modeling approaches have removed the sparsity constraint and shown superior performance over traditional CS and CPR techniques in recovering signals from fewer measurements. Generative model uses a pre-trained network, the generator of a Generative Adversarial Network (GAN) or the decoder of a Variational Autoencoder (VAE) to model the distribution of the signal and impose a Set-Restricted Eigenvalue Condition (S - REC) on the measurement operator. The S - REC property places a condition on the L2-norm of the difference in signal and measurement domain for signals coming from the set S. Solving CS and CPR using generative models have some limitations. The reconstructed signal is constrained to lie in the range-space of the generator. The reconstruction process is slow because the latent space is optimized through gradient-descent (GD) and requires several restarts. It has been argued that the distribution of natural images is not confined to a single manifold, but a union of submanifolds. To take advantage of this property, we propose a sparsity-driven latent space sampling (SDLSS) framework, where sparsity is imposed in the latent space. The effect is to divide the latent space into subspaces such that the generator models maps each subspace into a submanifold. We propose a proximal meta-learning (PML) algorithm to optimize the parameters of the generative model along with the latent code. The PML algorithm reduces the number of gradient steps required during testing and imposes sparsity in the latent space. We derive the sample complexity bounds within the SDLSS framework for the linear CS model, which is a generalization of the result available in the literature. The results demonstrate that, for a higher degree of compression, the SDLSS method is more efficient than the state-of-the-art deep compressive sensing (DCS) method. We consider both linear and learned nonlinear sensing mechanisms, where the nonlinear operator is a learned fully connected neural network or a convolutional neural network, and show that the learned nonlinear version is superior to the linear one. As an application of the nonlinear sensing operator, we consider compressive phase retrieval, wherein the problem is to reconstruct a signal from the magnitude of its compressed linear measurements. We adapt the S-REC imposed on the measurement operator and propose a novel cost function. The SDLSS framework along with PML algorithm is applied to optimize the sparse latent space such that the adapted S-REC loss and data-fitting error are minimized. The reconstruction process is fast and requires few gradient steps during testing compared with the state-of-art deep phase retrieval technique. Experiments are conducted on standard datasets such as MNIST, Fashion-MNIST, CIFAR-10, and CelebA to validate the efficiency of SDLSS framework for CS and CPR. The results show that, for a given dataset, there exists an effective input latent dimension for the generative model. Performance quantification is carried out by employing three objective metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and reconstruction error (RE) per pixel, which are averaged across the test dataset.
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Book chapters on the topic "Compressive phase retrieval"

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"Phase Retrieval." In Optical Compressive Imaging, 261–96. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.4324/9781315371474-14.

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Avirappattu, George. "On Efficient Acquisition and Recovery Methods for Certain Types of Big Data." In Big Data, 105–15. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch006.

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Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.
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Avirappattu, George. "On Efficient Acquisition and Recovery Methods for Certain Types of Big Data." In Advances in Public Policy and Administration, 137–47. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9649-5.ch008.

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Big data is characterized in many circles in terms of the three V's – volume, velocity and variety. Although most of us can sense palpable opportunities presented by big data there are overwhelming challenges, at many levels, turning such data into actionable information or building entities that efficiently work together based on it. This chapter discusses ways to potentially reduce the volume and velocity aspects of certain kinds of data (with sparsity and structure), while acquiring itself. Such reduction can alleviate the challenges to some extent at all levels, especially during the storage, retrieval, communication, and analysis phases. In this chapter we will conduct a non-technical survey, bringing together ideas from some recent and current developments. We focus primarily on Compressive Sensing and sparse Fast Fourier Transform or Sparse Fourier Transform. Almost all natural signals or data streams are known to have some level of sparsity and structure that are key for these efficiencies to take place.
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Conference papers on the topic "Compressive phase retrieval"

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Barbastathis, George, Justin W. Lee, Lei Tian, and Se Baek Oh. "Compressive Phase Retrieval." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/cosi.2011.cmc1.

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Moravec, Matthew L., Justin K. Romberg, and Richard G. Baraniuk. "Compressive phase retrieval." In Optical Engineering + Applications, edited by Dimitri Van De Ville, Vivek K. Goyal, and Manos Papadakis. SPIE, 2007. http://dx.doi.org/10.1117/12.736360.

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Barbastathis, George. "Compressive Phase Retrieval." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2015. http://dx.doi.org/10.1364/dh.2015.dt1a.1.

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Viswanathan, Aditya, and Mark Iwen. "Fast compressive phase retrieval." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421436.

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Gao, Yunhui, and Liangcai Cao. "High-throughput quantitative phase imaging via compressive phase retrieval." In Quantitative Phase Imaging IX, edited by YongKeun Park and Yang Liu. SPIE, 2023. http://dx.doi.org/10.1117/12.2655445.

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Bakhshizadeh, Milad, Arian Maleki, and Shirin Jalali. "Compressive Phase Retrieval of Structured Signals." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437687.

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Talegaonkar, Chinmay, Parthasarathi Khirwadkar, and Ajit Rajwade. "Compressive Phase Retrieval under Poisson Noise." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803017.

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Bodmann, Bernhard G., and Nathaniel Hammen. "Error bounds for noisy compressive phase retrieval." In 2015 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2015. http://dx.doi.org/10.1109/sampta.2015.7148909.

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Don, Michael, and Gonzalo Arce. "Antenna Pattern Measurement with Compressive Phase Retrieval." In 2020 IEEE Radio and Wireless Symposium (RWS). IEEE, 2020. http://dx.doi.org/10.1109/rws45077.2020.9050117.

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Li, Yi, and Vasileios Nakos. "Sublinear- Time Algorithms for Compressive Phase Retrieval." In 2018 IEEE International Symposium on Information Theory (ISIT). IEEE, 2018. http://dx.doi.org/10.1109/isit.2018.8437599.

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