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Journal articles on the topic 'Inverse imaging'

Author: Grafiati

Published: 14 March 2023

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1

Gkioulekas, Ioannis, Kavita Bala, Fredo Durand, Anat Levin, Shuang Zhao, and Todd Zickler. "Computational Imaging for Inverse Scattering." Electronic Imaging 2016, no. 9 (2016): 1. http://dx.doi.org/10.2352/issn.2470-1173.2016.9.mmrma-354.

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2

Schotland, John C. "Quantum imaging and inverse scattering." Optics Letters 35, no. 20 (2010): 3309. http://dx.doi.org/10.1364/ol.35.003309.

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3

Lavarello, Roberto J., and Michael L. Oelze. "Density imaging using inverse scattering." Journal of the Acoustical Society of America 125, no. 2 (2009): 793–802. http://dx.doi.org/10.1121/1.3050249.

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4

Ribes, Alejandro, and Francis Schmitt. "Linear inverse problems in imaging." IEEE Signal Processing Magazine 25, no. 4 (2008): 84–99. http://dx.doi.org/10.1109/msp.2008.923099.

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5

G., W., and Wolfgang-M. Boerner. "Inverse Methods in Electromagnetic Imaging." Mathematics of Computation 46, no. 174 (1986): 768. http://dx.doi.org/10.2307/2008025.

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6

Cameron, Maria, Sergey Fomel, and James Sethian. "Inverse problem in seismic imaging." PAMM 7, no. 1 (2007): 1024803–4. http://dx.doi.org/10.1002/pamm.200700601.

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7

Li, Jiahao, Mengwei Cao, Weili Liang, Yilin Zhang, Zhenwei Xie, and Xiaocong Yuan. "Inverse design of 1D color splitter for high-efficiency color imaging." Chinese Optics Letters 20, no. 7 (2022): 073601. http://dx.doi.org/10.3788/col202220.073601.

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8

Hedjazian, N., Y. Capdeville, and T. Bodin. "Multiscale seismic imaging with inverse homogenization." Geophysical Journal International 226, no. 1 (2021): 676–91. http://dx.doi.org/10.1093/gji/ggab121.

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Summary Seismic imaging techniques such as elastic full waveform inversion (FWI) have their spatial resolution limited by the maximum frequency present in the observed waveforms. Scales smaller than a fraction of the minimum wavelength cannot be resolved, and only a smoothed, effective version of the true underlying medium can be recovered. These finite-frequency effects are revealed by the upscaling or homogenization theory of wave propagation. Homogenization aims at computing larger scale effective properties of a medium containing small-scale heterogeneities. We study how this theory can be used in the context of FWI. The seismic imaging problem is broken down in a two-stage multiscale approach. In the first step, called homogenized FWI (HFWI), observed waveforms are inverted for a smooth, fully anisotropic effective medium, that does not contain scales smaller than the shortest wavelength present in the wavefield. The solution being an effective medium, it is difficult to directly interpret it. It requires a second step, called downscaling or inverse homogenization, where the smooth image is used as data, and the goal is to recover small-scale parameters. All the information contained in the observed waveforms is extracted in the HFWI step. The solution of the downscaling step is highly non-unique as many small-scale models may share the same long wavelength effective properties. We therefore rely on the introduction of external a priori information, and cast the problem in a Bayesian formulation. The ensemble of potential fine-scale models sharing the same long wavelength effective properties is explored with a Markov chain Monte Carlo algorithm. We illustrate the method with a synthetic cavity detection problem: we search for the position, size and shape of void inclusions in a homogeneous elastic medium, where the size of cavities is smaller than the resolving length of the seismic data. We illustrate the advantages of introducing the homogenization theory at both stages. In HFWI, homogenization acts as a natural regularization helping convergence towards meaningful solution models. Working with fully anisotropic effective media prevents the leakage of anisotropy induced by the fine scales into isotropic macroparameters estimates. In the downscaling step, the forward theory is the homogenization itself. It is computationally cheap, allowing us to consider geological models with more complexity (e.g. including discontinuities) and use stochastic inversion techniques.

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9

Bhat, Chandan, and Uday K. Khankhoje. "Inverse Imaging Using Total Field Measurements." IEEE Geoscience and Remote Sensing Letters 19 (2022): 1–5. http://dx.doi.org/10.1109/lgrs.2022.3158021.

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10

Chung, Francis J., and John C. Schotland. "Inverse Transport and Acousto-Optic Imaging." SIAM Journal on Mathematical Analysis 49, no. 6 (2017): 4704–21. http://dx.doi.org/10.1137/16m1104767.

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11

Xu, Gang, Mengdao Xing, Lei Zhang, Yabo Liu, and Yachao Li. "Bayesian Inverse Synthetic Aperture Radar Imaging." IEEE Geoscience and Remote Sensing Letters 8, no. 6 (2011): 1150–54. http://dx.doi.org/10.1109/lgrs.2011.2158797.

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12

Simonetti, Francesco. "Inverse scattering in modern ultrasound imaging." Journal of the Acoustical Society of America 123, no. 5 (2008): 3915. http://dx.doi.org/10.1121/1.2935927.

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13

De Micheli, Enrico, and Giovanni Alberto Viano. "Probabilistic regularization in inverse optical imaging." Journal of the Optical Society of America A 17, no. 11 (2000): 1942. http://dx.doi.org/10.1364/josaa.17.001942.

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14

Lin, Fa-Hsuan, Kevin W. K. Tsai, Ying-Hua Chu, et al. "Ultrafast inverse imaging techniques for fMRI." NeuroImage 62, no. 2 (2012): 699–705. http://dx.doi.org/10.1016/j.neuroimage.2012.01.072.

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15

Bryan, Kurt, and Lester F. Caudill, Jr. "An Inverse Problem in Thermal Imaging." SIAM Journal on Applied Mathematics 56, no. 3 (1996): 715–35. http://dx.doi.org/10.1137/s0036139994277828.

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16

Föcke, Janic, Daniel Baumgarten, and Martin Burger. "The inverse problem of magnetorelaxometry imaging." Inverse Problems 34, no. 11 (2018): 115008. http://dx.doi.org/10.1088/1361-6420/aadbbf.

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17

Gong, Wenlin. "High-resolution pseudo-inverse ghost imaging." Photonics Research 3, no. 5 (2015): 234. http://dx.doi.org/10.1364/prj.3.000234.

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18

Hasegawa, Tomonori, and Takashi Iwasaki. "Microwave imaging by quasi-inverse scattering." Electronics and Communications in Japan (Part I: Communications) 87, no. 5 (2004): 52–61. http://dx.doi.org/10.1002/ecja.10147.

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19

Wu, Xiaofei, Shiyuan Liu, Wen Lv, and Edmund Y. Lam. "Sparse nonlinear inverse imaging for shot count reduction in inverse lithography." Optics Express 23, no. 21 (2015): 26919. http://dx.doi.org/10.1364/oe.23.026919.

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20

ZANG Bo, 臧博, 郭睿 GUO Rui, 唐禹 TANG Yu, and 邢孟道 XING Meng-dao. "Real Envelope Imaging Algorithm for Inverse Synthetic Aperture Imaging Lidar." ACTA PHOTONICA SINICA 39, no. 12 (2010): 2152–57. http://dx.doi.org/10.3788/gzxb20103912.2152.

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21

Meijering, Anna E. C., Andreas S. Biebricher, Gerrit Sitters, et al. "Imaging unlabeled proteins on DNA with super-resolution." Nucleic Acids Research 48, no. 6 (2020): e34-e34. http://dx.doi.org/10.1093/nar/gkaa061.

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Abstract Fluorescence microscopy is invaluable to a range of biomolecular analysis approaches. The required labeling of proteins of interest, however, can be challenging and potentially perturb biomolecular functionality as well as cause imaging artefacts and photo bleaching issues. Here, we introduce inverse (super-resolution) imaging of unlabeled proteins bound to DNA. In this new method, we use DNA-binding fluorophores that transiently label bare DNA but not protein-bound DNA. In addition to demonstrating diffraction-limited inverse imaging, we show that inverse Binding-Activated Localization Microscopy or ‘iBALM’ can resolve biomolecular features smaller than the diffraction limit. The current detection limit is estimated to lie at features between 5 and 15 nm in size. Although the current image-acquisition times preclude super-resolving fast dynamics, we show that diffraction-limited inverse imaging can reveal molecular mobility at ∼0.2 s temporal resolution and that the method works both with DNA-intercalating and non-intercalating dyes. Our experiments show that such inverse imaging approaches are valuable additions to the single-molecule toolkit that relieve potential limitations posed by labeling.

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22

Zheng, Peixia, Qilong Tan, and Hong-chao Liu. "Inverse computational ghost imaging for image encryption." Optics Express 29, no. 14 (2021): 21290. http://dx.doi.org/10.1364/oe.428036.

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23

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.

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24

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.

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25

Barbone, Paul E., Gonzalo R. Feijóo, and Assad A. Oberai. "Krylov methods in inverse scattering and imaging." Journal of the Acoustical Society of America 130, no. 4 (2011): 2392. http://dx.doi.org/10.1121/1.3654589.

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26

Greensite, Fred. "Cardiac Electromagnetic Imaging as an Inverse Problem." Electromagnetics 21, no. 7-8 (2001): 559–77. http://dx.doi.org/10.1080/027263401752246207.

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27

Das, A., B. Sur, S. Yue, and G. Jonkmans. "Inverse Collimator-Based Radiation Imaging Detector System." AECL Nuclear Review 1, no. 1 (2012): 64–65. http://dx.doi.org/10.12943/anr.2012.00009.

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A radiation imaging system has been developed using the concept of inverse collimation, where a narrow shielding pencil is used instead of a classical collimator. This imaging detector is smaller, lighter and less expensive than a traditionally collimated detector, and can produce a spherical raster image of radiation sources in its surroundings. A prototype was developed at Atomic Energy of Canada Limited – Chalk River Laboratories, and the concept has been successfully proven in experiments using a point source as well as real sources in a high ambient field area. Such a radiation imaging system is effective in locating radiation sources in areas where accessibility is low and risk of radiological contamination is high, with applications in decontamination and decommissioning activities, nuclear material processing labs, etc.

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28

Xing, L., S. Hunjan, C. Cotrutz, et al. "Inverse planning for functional imaging-guided IMRT." International Journal of Radiation Oncology*Biology*Physics 54, no. 2 (2002): 34–35. http://dx.doi.org/10.1016/s0360-3016(02)03115-2.

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29

Abubakar, Aria, and Maokun Li. "Electromagnetic Inverse Problems for Sensing and Imaging." IEEE Antennas and Propagation Magazine 58, no. 2 (2016): 17. http://dx.doi.org/10.1109/map.2016.2520879.

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30

Vignaud, Luc. "Inverse synthetic aperture radar imaging of satellites." International Journal of Imaging Systems and Technology 9, no. 1 (1998): 24–28. http://dx.doi.org/10.1002/(sici)1098-1098(1998)9:1<24::aid-ima3>3.0.co;2-t.

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31

Sukharenko, Vitaly, and Roger Dorsinville. "Polarization Sensitive Imaging with Qubits." Applied Sciences 12, no. 4 (2022): 2027. http://dx.doi.org/10.3390/app12042027.

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We compare reconstructed quantum state images of a birefringent sample using direct quantum state tomography and inverse numerical optimization technique. Qubits are used to characterize birefringence in a flat transparent plastic sample by means of polarization sensitive measurement using density matrices of two-level quantum entangled photons. Pairs of entangled photons are generated in a type-II nonlinear crystal. About half of the generated photons interact with a birefringent sample, and coincidence counts are recorded. Coincidence rates of entangled photons are measured for a set of sixteen polarization states. Tomographic and inverse numerical techniques are used to reconstruct the density matrix, the degree of entanglement, and concurrence for each pixel of the investigated sample. An inverse numerical optimization technique is used to obtain a density matrix from measured coincidence counts with the maximum probability. Presented results highlight the experimental noise reduction, greater density matrix estimation, and overall image enhancement. The outcome of the entanglement distillation through projective measurements is a superposition of Bell states with different amplitudes. These changes are used to characterize the birefringence of a 3M tape. Well-defined concurrence and entanglement images of the birefringence are presented. Our results show that inverse numerical techniques improve overall image quality and detail resolution. The technique described in this work has many potential applications.

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32

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.

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In this work, a generalized scheme of regularization of inverse problems is considered, where a priori knowledge about the smoothness of the solution is given by means of some self-adjoint operator in the solution space. The formulation of the problem is considered, namely, in addition to the main inverse problem, an additional problem is defined, in which the solution is the right-hand side of the equation. Thus, for the regularization of the main inverse problem, an additional inverse problem is used, which brings information about the smoothness of the solution to the initial problem. This formulation of the problem makes it possible to use operators of high complexity for regularization of inverse problems, which is an urgent need in modern machine learning problems, in particular, in image processing problems. The paper examines the approximation error of the solution of the initial problem using an additional problem.

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33

Tomei, Sonia, Alessio Bacci, Elisa Giusti, Marco Martorella, and Fabrizio Berizzi. "Compressive sensing‐based inverse synthetic radar imaging imaging from incomplete data." IET Radar, Sonar & Navigation 10, no. 2 (2016): 386–97. http://dx.doi.org/10.1049/iet-rsn.2015.0290.

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34

Schlegel, Wolfgang, and Peter Kneschaurek. "Inverse bestrahlungsplanung." Strahlentherapie und Onkologie 175, no. 5 (1999): 197–207. http://dx.doi.org/10.1007/bf02742396.

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35

Behura, Jyoti, Kees Wapenaar, and Roel Snieder. "Autofocus Imaging: Image reconstruction based on inverse scattering theory." GEOPHYSICS 79, no. 3 (2014): A19—A26. http://dx.doi.org/10.1190/geo2013-0398.1.

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Conventional imaging algorithms assume single scattering and therefore cannot image multiply scattered waves correctly. The multiply scattered events in the data are imaged at incorrect locations resulting in spurious subsurface structures and erroneous interpretation. This drawback of current migration/imaging algorithms is especially problematic for regions where illumination is poor (e.g., subsalt), in which the spurious events can mask true structure. Here we discuss an imaging technique that not only images primaries but also internal multiples accurately. Using only surface-reflection data and direct-arrivals, we generate the up- and down-going wavefields at every image point in the subsurface. An imaging condition is applied to these up- and down-going wavefields directly to generate the image. Because the above algorithm is based on inverse-scattering theory, the reconstructed wavefields are accurate and contain multiply scattered energy in addition to the primary event. As corroborated by our synthetic examples, imaging of these multiply scattered energy helps eliminate spurious reflectors in the image. Other advantages of this imaging algorithm over existing imaging algorithms include more accurate amplitudes, target-oriented imaging, and a highly parallelizable algorithm.

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36

Stefanov, Plamen, and Gunther Uhlmann. "An inverse source problem in optical molecular imaging." Analysis & PDE 1, no. 1 (2008): 115–26. http://dx.doi.org/10.2140/apde.2008.1.115.

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37

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.

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38

Shibuya, Kengo, Haruo Saito, Hideaki Tashima, and Taiga Yamaya. "Using inverse Laplace transform in positronium lifetime imaging." Physics in Medicine & Biology 67, no. 2 (2022): 025009. http://dx.doi.org/10.1088/1361-6560/ac499b.

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Abstract Positronium (Ps) lifetime imaging is gaining attention to bring out additional biomedical information from positron emission tomography (PET). The lifetime of Ps in vivo can change depending on the physical and chemical environments related to some diseases. Due to the limited sensitivity, Ps lifetime imaging may require merging some voxels for statistical accuracy. This paper presents a method for separating the lifetime components in the voxel to avoid information loss due to averaging. The mathematics for this separation is the inverse Laplace transform (ILT), and the authors examined an iterative numerical ILT algorithm using Tikhonov regularization, namely CONTIN, to discriminate a small lifetime difference due to oxygen saturation. The separability makes it possible to merge voxels without missing critical information on whether they contain abnormally long or short lifetime components. The authors conclude that ILT can compensate for the weaknesses of Ps lifetime imaging and extract the maximum amount of information.

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39

Zhang, Chi, Shuxu Guo, Junsheng Cao, Jian Guan, and Fengli Gao. "Object reconstitution using pseudo-inverse for ghost imaging." Optics Express 22, no. 24 (2014): 30063. http://dx.doi.org/10.1364/oe.22.030063.

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40

Alvarez, Y., J. Laviada, L. Tirado, et al. "Inverse Fast Multipole Method for Monostatic Imaging Applications." IEEE Geoscience and Remote Sensing Letters 10, no. 5 (2013): 1239–43. http://dx.doi.org/10.1109/lgrs.2012.2237158.

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41

Alvarez, Y., J. A. Martinez, F. Las-Heras, and C. M. Rappaport. "An Inverse Fast Multipole Method for Imaging Applications." IEEE Antennas and Wireless Propagation Letters 10 (2011): 1259–62. http://dx.doi.org/10.1109/lawp.2011.2175477.

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42

Bao, Zheng. "Inverse synthetic aperture radar imaging of maneuvering targets." Optical Engineering 37, no. 5 (1998): 1582. http://dx.doi.org/10.1117/1.601670.

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43

Bryan, Kurt, and Tanya Leise. "Impedance Imaging, Inverse Problems, and Harry Potter's Cloak." SIAM Review 52, no. 2 (2010): 359–77. http://dx.doi.org/10.1137/090757873.

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44

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.

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45

Huang, Ya Jing, Xuezhi Wang, Xiang Li, and Bill Moran. "Inverse Synthetic Aperture Radar Imaging Using Frame Theory." IEEE Transactions on Signal Processing 60, no. 10 (2012): 5191–200. http://dx.doi.org/10.1109/tsp.2012.2208107.

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46

She, Zhishun, D. A. Gray, and R. E. Bogner. "Autofocus for inverse synthetic aperture radar (ISAR) imaging." Signal Processing 81, no. 2 (2001): 275–91. http://dx.doi.org/10.1016/s0165-1684(00)00207-3.

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47

Burfeindt, Matthew J., Jacob D. Shea, Barry D. Van Veen, and Susan C. Hagness. "Beamforming-Enhanced Inverse Scattering for Microwave Breast Imaging." IEEE Transactions on Antennas and Propagation 62, no. 10 (2014): 5126–32. http://dx.doi.org/10.1109/tap.2014.2344096.

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48

Fedchuk, Andriy, Iryna Bartsykovska, Alla Fedchuk, and Oleksandr Fedchuk. "Inverse Wavelet Transform in Virus–Cell Interaction Imaging." Antiviral Research 78, no. 2 (2008): A38. http://dx.doi.org/10.1016/j.antiviral.2008.01.070.

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49

Gryazin, Yuriy A., Michael V. Klibanov, and Thomas R. Lucas. "Numerical Solution of a Subsurface Imaging Inverse Problem." SIAM Journal on Applied Mathematics 62, no. 2 (2001): 664–83. http://dx.doi.org/10.1137/s0036139900377366.

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50

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 (2020): 39–56. http://dx.doi.org/10.1109/jsait.2020.2991563.

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