Academic literature on the topic 'Imaging inverse problems'
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Journal articles on the topic "Imaging inverse problems":
Ribes, Alejandro, and Francis Schmitt. "Linear inverse problems in imaging." IEEE Signal Processing Magazine 25, no. 4 (July 2008): 84–99. http://dx.doi.org/10.1109/msp.2008.923099.
Gilton, Davis, Gregory Ongie, and Rebecca Willett. "Model Adaptation for Inverse Problems in Imaging." IEEE Transactions on Computational Imaging 7 (2021): 661–74. http://dx.doi.org/10.1109/tci.2021.3094714.
Oksanen, Lauri, and Mikko Salo. "Inverse problems in imaging and engineering science." Mathematics in Engineering 2, no. 2 (2020): 287–89. http://dx.doi.org/10.3934/mine.2020014.
Abubakar, Aria, and Maokun Li. "Electromagnetic Inverse Problems for Sensing and Imaging." IEEE Antennas and Propagation Magazine 58, no. 2 (April 2016): 17. http://dx.doi.org/10.1109/map.2016.2520879.
Kravchuk, Oleg, and Galyna Kriukova. "Regularization by Denoising for Inverse Problems in Imaging." Mohyla Mathematical Journal 5 (December 28, 2022): 57–61. http://dx.doi.org/10.18523/2617-70805202257-61.
Gilton, Davis, Gregory Ongie, and Rebecca Willett. "Deep Equilibrium Architectures for Inverse Problems in Imaging." IEEE Transactions on Computational Imaging 7 (2021): 1123–33. http://dx.doi.org/10.1109/tci.2021.3118944.
Bryan, Kurt, and Tanya Leise. "Impedance Imaging, Inverse Problems, and Harry Potter's Cloak." SIAM Review 52, no. 2 (January 2010): 359–77. http://dx.doi.org/10.1137/090757873.
Gilton, Davis, Greg Ongie, and Rebecca Willett. "Neumann Networks for Linear Inverse Problems in Imaging." IEEE Transactions on Computational Imaging 6 (2020): 328–43. http://dx.doi.org/10.1109/tci.2019.2948732.
Ongie, Gregory, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, and Rebecca Willett. "Deep Learning Techniques for Inverse Problems in Imaging." IEEE Journal on Selected Areas in Information Theory 1, no. 1 (May 2020): 39–56. http://dx.doi.org/10.1109/jsait.2020.2991563.
Habring, Andreas, and Martin Holler. "A Generative Variational Model for Inverse Problems in Imaging." SIAM Journal on Mathematics of Data Science 4, no. 1 (March 2022): 306–35. http://dx.doi.org/10.1137/21m1414978.
Dissertations / Theses on the topic "Imaging inverse problems":
Leung, Wun Ying Valerie. "Inverse problems in astronomical and general imaging." Thesis, University of Canterbury. Electrical and Computer Engineering, 2002. http://hdl.handle.net/10092/7513.
Szasz, Teodora. "Advanced beamforming techniques in ultrasound imaging and the associated inverse problems." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30221/document.
Ultrasound (US) allows non-invasive and ultra-high frame rate imaging procedures at reduced costs. Cardiac, abdominal, fetal, and breast imaging are some of the applications where it is extensively used as diagnostic tool. In a classical US scanning process, short acoustic pulses are transmitted through the region-of-interest of the human body. The backscattered echo signals are then beamformed for creating radiofrequency(RF) lines. Beamforming (BF) plays a key role in US image formation, influencing the resolution and the contrast of final image. The objective of this thesis is to model BF as an inverse problem, relating the raw channel data to the signals to be recovered. The proposed BF framework improves the contrast and the spatial resolution of the US images, compared with the existing BF methods. To begin with, we investigated the existing BF methods in medical US imaging. We briefly review the most common BF techniques, starting with the standard delay-and-sum BF method and emerging to the most known adaptive BF techniques, such as minimum variance BF. Afterwards, we investigated the use of sparse priors in creating original two-dimensional beamforming methods for ultrasound imaging. The proposed approaches detect the strong reflectors from the scanned medium based on the well-known Bayesian Information Criteria used in statistical modeling. Furthermore, we propose a new way of addressing the BF in US imaging, by formulating it as a linear inverse problem relating the reflected echoes to the signal to be recovered. Our approach offers flexibility in the choice of statistical assumptions on the signal to be beamformed and it is robust to a reduced number of pulse emissions. At the end of this research, we investigated the use of the non-Gaussianity properties of the RF signals in the BF process, by assuming alpha-stable statistics of US images
Gregson, James. "Applications of inverse problems in fluids and imaging." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54081.
Science, Faculty of
Computer Science, Department of
Graduate
Lecharlier, Loïc. "Blind inverse imaging with positivity constraints." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209240.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Zhang, Wenlong. "Forward and Inverse Problems Under Uncertainty." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE024/document.
This thesis contains two different subjects. In first part, two cases are considered. One is the thin plate spline smoother model and the other one is the elliptic boundary equations with uncertain boundary data. In this part, stochastic convergences of the finite element methods are proved for each problem.In second part, we provide a mathematical analysis of the linearized inverse problem in multifrequency electrical impedance tomography. We present a mathematical and numerical framework for a procedure of imaging anisotropic electrical conductivity tensor using a novel technique called Diffusion Tensor Magneto-acoustography and propose an optimal control approach for reconstructing the cross-property factor relating the diffusion tensor to the anisotropic electrical conductivity tensor. We prove convergence and Lipschitz type stability of the algorithm and present numerical examples to illustrate its accuracy. The cell model for Electropermeabilization is demonstrated. We study effective parameters in a homogenization model. We demonstrate numerically the sensitivity of these effective parameters to critical microscopic parameters governing electropermeabilization
Zhu, Sha. "A Bayesian Approach for Inverse Problems in Synthetic Aperture Radar Imaging." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00844748.
Alfowzan, Mohammed Fowzan, and Mohammed Fowzan Alfowzan. "Solutions to Space-Time Inverse Problems." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/621791.
Rückert, Nadja. "Studies on two specific inverse problems from imaging and finance." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-91587.
Som, Subhojit. "Topics in Sparse Inverse Problems and Electron Paramagnetic Resonance Imaging." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282135281.
Zamanian, Sam Ahmad. "Hierarchical Bayesian approaches to seismic imaging and other geophysical inverse problems." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/92970.
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 (pages 189-196).
In many geophysical inverse problems, smoothness assumptions on the underlying geologic model are utilized to mitigate the effects of poor data coverage and observational noise and to improve the quality of the inferred model parameters. In the context of Bayesian inference, these smoothness assumptions take the form of a prior distribution on the model parameters. Conventionally, the regularization parameters defining these assumptions are fixed independently from the data or tuned in an ad hoc manner. However, it is often the case that the smoothness properties of the true earth model are not known a priori, and furthermore, these properties may vary spatially. In the seismic imaging problem, for example, where the objective is to estimate the earth's reflectivity, the reflectivity model is smooth along a particular reflector but exhibits a sharp contrast in the direction orthogonal to the reflector. In such cases, defining a prior using predefined smoothness assumptions may result in posterior estimates of the model that incorrectly smooth out these sharp contrasts. In this thesis, we explore the application of Bayesian inference to different geophysical inverse problems and seek to address issues related to smoothing by appealing to the hierarchical Bayesian framework. We capture the smoothness properties of the prior distribution on the model by defining a Markov random field (MRF) on the set of model parameters and assigning weights to the edges of the underlying graph; we refer to these parameters as the edge strengths of the MRF. We investigate two cases where the smoothing is specified a priori and introduce a method for estimating the edge strengths of the MRF. In the first part of this thesis, we apply a Bayesian inference framework (where the edge strengths of the MRF are predetermined) to the problem of characterizing the fractured nature of a reservoir from seismic data. Our methodology combines different features of the seismic data, particularly P-wave reflection amplitudes and scattering attributes, to allow for estimation of fracture properties under a larger physical regime than would be attainable using only one of these data types. Through this application, we demonstrate the capability of our parameterization of the prior distribution with edge strengths to both enforce smoothness in the estimates of the fracture properties and capture a priori information about geological features in the model (such as a discontinuity that may arise in the presence of a fault). We solve the inference problem via loopy belief propagation to approximate the posterior marginal distributions of the fracture properties, as well as their maximum a posteriori (MAP) and Bayes least squares estimates. In the second part of the thesis, we investigate how the parameters defining the prior distribution are connected to the model covariance and address the question of how to optimize these parameters in the context of the seismic imaging problem. We formulate the seismic imaging problem within the hierarchical Bayesian setting, where the edge strengths are treated as random variables to be inferred from the data, and provide a framework for computing the marginal MAP estimate of the edge strengths by application of the expectation-maximization (E-M) algorithm. We validate our methodology on synthetic datasets arising from 2-D models. The images we obtain after inferring the edge strengths exhibit the desired spatially-varying smoothness properties and yield sharper, more coherent reflectors. In the final part of the thesis, we shift our focus and consider the problem of timelapse seismic processing, where the objective is to detect changes in the subsurface over a period of time using repeated seismic surveys. We focus on the realistic case where the surveys are taken with differing acquisition geometries. In such situations, conventional methods for processing time-lapse data involve inverting surveys separately and subtracting the inversion models to estimate the change in model parameters; however, such methods often perform poorly as they do not correctly account for differing model uncertainty between surveys due to differences in illumination and observational noise. Applying the machinery explored in the previous chapters, we formulate the time-lapse processing problem within the hierarchical Bayesian setting and present a framework for computing the marginal MAP estimate of the time-lapse change model using the E-M algorithm. The results of our inference framework are validated on synthetic data from a 2-D time-lapse seismic imaging example, where the hierarchical Bayesian estimates significantly outperform conventional time-lapse inversion results.
by Sam Ahmad Zamanian.
Ph. D.
Books on the topic "Imaging inverse problems":
Carpio, Ana, Oliver Dorn, Miguel Moscoso, Frank Natterer, George C. Papanicolaou, Maria Luisa Rapún, and Alessandro Teta. Inverse Problems and Imaging. Edited by Luis L. Bonilla. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7.
F, Roach G., ed. Inverse problems and imaging. Harlow, Essex, England: Longman Scientific & Technical, 1991.
Seo, Jin Keun, and Eung Je Woo. Nonlinear Inverse Problems in Imaging. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118478141.
Bertero, Mario. Introduction to inverse problems in imaging. Bristol, UK: Institute of Physics Pub., 1998.
Nashed, M. Zuhair, and Otmar Scherzer, eds. Inverse Problems, Image Analysis, and Medical Imaging. Providence, Rhode Island: American Mathematical Society, 2002. http://dx.doi.org/10.1090/conm/313.
Donatelli, Marco, and Stefano Serra-Capizzano, eds. Computational Methods for Inverse Problems in Imaging. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32882-5.
Chalmond, Bernard. Modeling and Inverse Problems in Imaging Analysis. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21662-1.
Chalmond, Bernard. Modeling and Inverse Problems in Imaging Analysis. New York, NY: Springer New York, 2003.
Chalmond, Bernard. Modeling and inverse problems in imaging analysis. New York: Springer, 2003.
Engl, Heinz W., Alfred K. Louis, and William Rundell, eds. Inverse Problems in Medical Imaging and Nondestructive Testing. Vienna: Springer Vienna, 1997. http://dx.doi.org/10.1007/978-3-7091-6521-8.
Book chapters on the topic "Imaging inverse problems":
Groetsch, Charles. "Linear Inverse Problems." In Handbook of Mathematical Methods in Imaging, 3–41. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-92920-0_1.
Groetsch, Charles. "Linear Inverse Problems." In Handbook of Mathematical Methods in Imaging, 3–46. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-0790-8_1.
Moscoso, Miguel. "Polarization-Based Optical Imaging." In Inverse Problems and Imaging, 67–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_4.
Moscoso, Miguel. "Introduction to Image Reconstruction." In Inverse Problems and Imaging, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_1.
Natterer, Frank. "X-ray Tomography." In Inverse Problems and Imaging, 17–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_2.
Dorn, Oliver, Hugo Bertete-Aguirre, and George C. Papanicolaou. "Adjoint Fields and Sensitivities for 3D Electromagnetic Imaging in Isotropic and Anisotropic Media." In Inverse Problems and Imaging, 35–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_3.
Carpio, Ana, and Maria Luisa Rapún. "Topological Derivatives for Shape Reconstruction." In Inverse Problems and Imaging, 85–133. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_5.
Dorn, Oliver. "Time-Reversal and the Adjoint Imaging Method with an Application in Telecommunication." In Inverse Problems and Imaging, 135–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_6.
Dell'Antonio, Gianfausto, Rodolfo Figari, and Alessandro Teta. "A Brief Review on Point Interactions." In Inverse Problems and Imaging, 171–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78547-7_7.
Beilina, Larisa, and Michael V. Klibanov. "Approximate Global Convergence in Imaging of Land Mines from Backscattered Data." In Applied Inverse Problems, 15–36. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7816-4_2.
Conference papers on the topic "Imaging inverse problems":
Bala, Raja. "Inverse problems in color device characterization." In Electronic Imaging 2003, edited by Charles A. Bouman and Robert L. Stevenson. SPIE, 2003. http://dx.doi.org/10.1117/12.488617.
Oberai, Assad A. "Inverse problems biomechanical imaging (Conference Presentation)." In Optical Elastography and Tissue Biomechanics III, edited by Kirill V. Larin and David D. Sampson. SPIE, 2016. http://dx.doi.org/10.1117/12.2216702.
Weiss, Pierre. "The geometry of convex regularized inverse problems." In Mathematics in Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/math.2018.mw2d.5.
Oh, Seungseok, Adam B. Milstein, Charles A. Bouman, and Kevin J. Webb. "Multigrid algorithms for optimization and inverse problems." In Electronic Imaging 2003, edited by Charles A. Bouman and Robert L. Stevenson. SPIE, 2003. http://dx.doi.org/10.1117/12.484805.
Marais, Willem, Robert Holz, Yu Hen Hu, and Rebecca Willett. "Atmospheric lidar imaging and poisson inverse problems." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532504.
Liu, Jiaming, Yu Sun, Weijie Gan, Xiaojian Xu, Brendt Wohlberg, and Ulugbek S. Kamilov. "Stochastic Deep Unfolding for Imaging Inverse Problems." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414332.
"Deep Learning for Inverse Problems in Imaging." In 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2019. http://dx.doi.org/10.1109/ipta.2019.8936102.
Sinha, Ayan T., Justin Lee, Shuai Li, and George Barbastathis. "Solving inverse problems using residual neural networks." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/dh.2017.w1a.3.
McCormick, N. J. "Source Estimation in Inverse Radiative Transfer Problems." In Advances in Optical Imaging and Photon Migration. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/aoipm.1994.ncpdir.207.
Oliveri, Giacomo, and Toshifumi Moriyama. "Compressive Sensing Methods Applied to Inverse Imaging Problems." In Computational Optical Sensing and Imaging. Washington, D.C.: OSA, 2014. http://dx.doi.org/10.1364/cosi.2014.cw2c.3.
Reports on the topic "Imaging inverse problems":
Schotland, John C. Inverse Problems and Optical Imaging with Nanoscale Resolution. Fort Belvoir, VA: Defense Technical Information Center, March 2010. http://dx.doi.org/10.21236/ada565342.
Prasad, S. Post Detection Processing and Inverse Problems in Ground Based Imaging. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada409722.
Fowler, Michael James. Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging. Office of Scientific and Technical Information (OSTI), April 2014. http://dx.doi.org/10.2172/1179471.
Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.