Relevant bibliographies by topics / Inverse imaging

Academic literature on the topic 'Inverse imaging'

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Published: 14 March 2023

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Journal articles on the topic "Inverse imaging"

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Gkioulekas, Ioannis, Kavita Bala, Fredo Durand, Anat Levin, Shuang Zhao, and Todd Zickler. "Computational Imaging for Inverse Scattering." Electronic Imaging 2016, no. 9 (February 15, 2016): 1. http://dx.doi.org/10.2352/issn.2470-1173.2016.9.mmrma-354.

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Schotland, John C. "Quantum imaging and inverse scattering." Optics Letters 35, no. 20 (October 7, 2010): 3309. http://dx.doi.org/10.1364/ol.35.003309.

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

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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.

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

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

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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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Hedjazian, N., Y. Capdeville, and T. Bodin. "Multiscale seismic imaging with inverse homogenization." Geophysical Journal International 226, no. 1 (March 27, 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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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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Chung, Francis J., and John C. Schotland. "Inverse Transport and Acousto-Optic Imaging." SIAM Journal on Mathematical Analysis 49, no. 6 (January 2017): 4704–21. http://dx.doi.org/10.1137/16m1104767.

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Dissertations / Theses on the topic "Inverse imaging"

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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.

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Dans les problèmes inverses en imagerie, on suppose généralement connu l’opérateur ou matrice décrivant le système de formation de l’image. De façon équivalente pour un système linéaire, on suppose connue sa réponse impulsionnelle. Toutefois, ceci n’est pas une hypothèse réaliste pour de nombreuses applications pratiques pour lesquelles cet opérateur n’est en fait pas connu (ou n’est connu qu’approximativement). On a alors affaire à un problème d’inversion dite “aveugle”. Dans le cas de systèmes invariants par translation, on parle de “déconvolution aveugle” car à la fois l’image ou objet de départ et la réponse impulsionnelle doivent être estimées à partir de la seule image observée qui résulte d’une convolution et est affectée d’erreurs de mesure. Ce problème est notoirement difficile et pour pallier les ambiguïtés et les instabilités numériques inhérentes à ce type d’inversions, il faut recourir à des informations ou contraintes supplémentaires, telles que la positivité qui s’est avérée un levier de stabilisation puissant dans les problèmes d’imagerie non aveugle. La thèse propose de nouveaux algorithmes d’inversion aveugle dans un cadre discret ou discrétisé, en supposant que l’image inconnue, la matrice à inverser et les données sont positives. Le problème est formulé comme un problème d’optimisation (non convexe) où le terme d’attache aux données à minimiser, modélisant soit le cas de données de type Poisson (divergence de Kullback-Leibler) ou affectées de bruit gaussien (moindres carrés), est augmenté par des termes de pénalité sur les inconnues du problème. La stratégie d’optimisation consiste en des ajustements alternés de l’image à reconstruire et de la matrice à inverser qui sont de type multiplicatif et résultent de la minimisation de fonctions coût “surrogées” valables dans le cas positif. Le cadre assez général permet d’utiliser plusieurs types de pénalités, y compris sur la variation totale (lissée) de l’image. Une normalisation éventuelle de la réponse impulsionnelle ou de la matrice est également prévue à chaque itération. Des résultats de convergence pour ces algorithmes sont établis dans la thèse, tant en ce qui concerne la décroissance des fonctions coût que la convergence de la suite des itérés vers un point stationnaire. La méthodologie proposée est validée avec succès par des simulations numériques relatives à différentes applications telle que la déconvolution aveugle d'images en astronomie, la factorisation en matrices positives pour l’imagerie hyperspectrale et la déconvolution de densités en statistique.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
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Javanmard, Mehdi. "Inverse problem approach to ultrasound medical imaging." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0001/NQ31933.pdf.

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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.

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The resolution and the quality of an imaged object are limited by four contributing factors. Firstly, the primary resolution limit of a system is imposed by the aperture of an instrument due to the effects of diffraction. Secondly, the finite sampling frequency, the finite measurement time and the mechanical limitations of the equipment also affect the resolution of the images captured. Thirdly, the images are corrupted by noise, a process inherent to all imaging systems. Finally, a turbulent imaging medium introduces random degradations to the signals before they are measured. In astronomical imaging, it is the atmosphere which distorts the wavefronts of the objects, severely limiting the resolution of the images captured by ground-based telescopes. These four factors affect all real imaging systems to varying degrees. All the limitations imposed on an imaging system result in the need to deduce or reconstruct the underlying object distribution from the distorted measured data. This class of problems is called inverse problems. The key to the success of solving an inverse problem is the correct modelling of the physical processes which give rise to the corresponding forward problem. However, the physical processes have an infinite amount of information, but only a finite number of parameters can be used in the model. Information loss is therefore inevitable. As a result, the solution to many inverse problems requires additional information or prior knowledge. The application of prior information to inverse problems is a recurrent theme throughout this thesis. An inverse problem that has been an active research area for many years is interpolation, and there exist numerous techniques for solving this problem. However, many of these techniques neither account for the sampling process of the instrument nor include prior information in the reconstruction. These factors are taken into account in the proposed optimal Bayesian interpolator. The process of interpolation is also examined from the point of view of superresolution, as these processes can be viewed as being complementary. Since the principal effect of atmospheric turbulence on an incoming wavefront is a phase distortion, most of the inverse problem techniques devised for this seek to either estimate or compensate for this phase component. These techniques are classified into computer post-processing methods, adaptive optics (AO) and hybrid techniques. Blind deconvolution is a post-processing technique which uses the speckle images to estimate both the object distribution and the point spread function (PSF), the latter of which is directly related to the phase. The most successful approaches are based on characterising the PSF as the aberrations over the aperture. Since the PSF is also dependent on the atmosphere, it is possible to constrain the solution using the statistics of the atmosphere. An investigation shows the feasibility of this approach. Bispectrum is also a post-processing method which reconstructs the spectrum of the object. The key component for phase preservation is the property of phase closure, and its application as prior information for blind deconvolution is examined. Blind deconvolution techniques utilise only information in the image channel to estimate the phase which is difficult. An alternative method for phase estimation is from a Shack-Hartmann (SH) wavefront sensing channel. However, since phase information is present in both the wavefront sensing and the image channels simultaneously, both of these approaches suffer from the problem that phase information from only one channel is used. An improved estimate of the phase is achieved by a combination of these methods, ensuring that the phase estimation is made jointly from the data in both the image and the wavefront sensing measurements. This formulation, posed as a blind deconvolution framework, is investigated in this thesis. An additional advantage of this approach is that since speckle images are imaged in a narrowband, while wavefront sensing images are captured by a charge-coupled device (CCD) camera at all wavelengths, the splitting of the light does not compromise the light level for either channel. This provides a further incentive for using simultaneous data sets. The effectiveness of using Shack-Hartmann wavefront sensing data for phase estimation relies on the accuracy of locating the data spots. The commonly used method which calculates the centre of gravity of the image is in fact prone to noise and is suboptimal. An improved method for spot location based on blind deconvolution is demonstrated. Ground-based adaptive optics (AO) technologies aim to correct for atmospheric turbulence in real time. Although much success has been achieved, the space- and time-varying nature of the atmosphere renders the accurate measurement of atmospheric properties difficult. It is therefore usual to perform additional post-processing on the AO data. As a result, some of the techniques developed in this thesis are applicable to adaptive optics. One of the methods which utilise elements of both adaptive optics and post-processing is the hybrid technique of deconvolution from wavefront sensing (DWFS). Here, both the speckle images and the SH wavefront sensing data are used. The original proposal of DWFS is simple to implement but suffers from the problem where the magnitude of the object spectrum cannot be reconstructed accurately. The solution proposed for overcoming this is to use an additional set of reference star measurements. This however does not completely remove the original problem; in addition it introduces other difficulties associated with reference star measurements such as anisoplanatism and reduction of valuable observing time. In this thesis a parameterised solution is examined which removes the need for a reference star, as well as offering a potential to overcome the problem of estimating the magnitude of the object.
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Papadopoulos, Timoleon. "Inverse filtering for virtual acoustic imaging systems." Thesis, University of Southampton, 2006. https://eprints.soton.ac.uk/157421/.

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The research topic of this thesis is the use of inverse filtering for the design and implementation of two-channel virtual acoustic imaging systems that utilise loudspeakers. The basic objective of such systems is to invert the electroacoustic plant between the input to the loudspeakers and the output at the listener’s ears and hence make it possible for a pair of binaural signals to be locally reproduced at the position of the listener’s ears. As a starting point for the research presented, a previously introduced type of inverse filtering design is considered in which the inverse is implemented with FIR filters. The basic formulation of this design is described and a number of innovative points regarding its implementation are made. An experimental procedure is then formulated for the evaluation of the effectiveness of this inverse filtering design that is based on objective measurements of the inversion process. Unlike previously employed methods that are based on computer simulations or subjective experiments, the introduced experimental procedure is shown to be very efficient in isolating and exactly quantifying the effect on the accuracy of the inversion of a number of errors and approximations typically present in the implementation. A detailed evaluation is thus presented of the inverse filtering design at hand in realistic conditions of implementation. Subsequently, a novel method for the off-line implementation of the inverse filtering is presented that utilises recursive filters of lower order. In this method, the responses of the inverse filters are decomposed into two parts, one realisable in forward time and one in backward time. The effectiveness of this new method for the implementation of the inverse is tested and compared with a small selection of the objective evaluation results described above. Finally, an algorithm for the on-line implementation of the forward-backward inverse filtering is proposed and its computational cost is compared with the currently available frequency-domain block-processing filtering algorithms.
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Gregson, James. "Applications of inverse problems in fluids and imaging." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/54081.

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Three applications of inverse problems relating to fluid imaging and image deblurring are presented. The first two, tomographic reconstruction of dye concentration fields from multi-view video and deblurring of photographs, are addressed by a stochastic optimization scheme that allows a wide variety of priors to be incorporated into the reconstruction process within a straightforward framework. The third, estimation of fluid velocities from volumetric dye concentration fields, highlights a previously unexplored connection between fluid simulation and proximal algorithms from convex optimization. This connection allows several classical imaging inverse problems to be investigated in the context of fluids, including optical flow, denoising and deconvolution. The connection also allows inverse problems to be incorporated into fluid simulation for the purposes of physically-based regularization of optical flow and for stylistic modifications of fluid captures. Through both methods and all three applications the importance of incorporating domain-specific priors into inverse problems for fluids and imaging is highlighted.
Science, Faculty of
Computer Science, Department of
Graduate
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Li, Xiaobei. "Instrumentation and inverse problem solving for impedance imaging /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/5973.

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Oster, Howard Steven. "Electrocardiographic imaging: New applications and new inverse methodology." Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1058380620.

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Szasz, Teodora. "Advanced beamforming techniques in ultrasound imaging and the associated inverse problems." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30221/document.

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L'imagerie ultrasonore (US) permet de réaliser des examens médicaux non invasifs avec des méthodes d'acquisition rapides à des coûts modérés. L'imagerie cardiaque, abdominale, fœtale, ou mammaire sont quelques-unes des applications où elle est largement utilisée comme outil de diagnostic. En imagerie US classique, des ondes acoustiques sont transmises à une région d'intérêt du corps humain. Les signaux d'écho rétrodiffusés, sont ensuite formés pour créer des lignes radiofréquences. La formation de voies (FV) joue un rôle clé dans l'obtention des images US, car elle influence la résolution et le contraste de l'image finale. L'objectif de ce travail est de modéliser la formation de voies comme un problème inverse liant les données brutes aux signaux RF. Le modèle de formation de voies proposé ici améliore le contraste et la résolution spatiale des images échographiques par rapport aux techniques de FV existants. Dans un premier temps, nous nous sommes concentrés sur des méthodes de FV en imagerie US. Nous avons brièvement passé en revue les techniques de formation de voies les plus courantes, en commencent par la méthode par retard et somme standard puis en utilisant les techniques de formation de voies adaptatives. Ensuite, nous avons étudié l'utilisation de signaux qui exploitent une représentation parcimonieuse de l'image US dans le cadre de la formation de voies. Les approches proposées détectent les réflecteurs forts du milieu sur la base de critères bayésiens. Nous avons finalement développé une nouvelle façon d'aborder la formation de voies en imagerie US, en la formulant comme un problème inverse linéaire liant les échos réfléchis au signal final. L'intérêt majeur de notre approche est la flexibilité dans le choix des hypothèses statistiques sur le signal avant la formation de voies et sa robustesse dans à un nombre réduit d'émissions. Finalement, nous présentons une nouvelle méthode de formation de voies pour l'imagerie US basée sur l'utilisation de caractéristique statistique des signaux supposée alpha-stable
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
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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.

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Synthetic Aperture Radar (SAR) imaging is a well-known technique in the domain of remote sensing, aerospace surveillance, geography and mapping. To obtain images of high resolution under noise, taking into account of the characteristics of targets in the observed scene, the different uncertainties of measure and the modeling errors becomes very important.Conventional imaging methods are based on i) over-simplified scene models, ii) a simplified linear forward modeling (mathematical relations between the transmitted signals, the received signals and the targets) and iii) using a very simplified Inverse Fast Fourier Transform (IFFT) to do the inversion, resulting in low resolution and noisy images with unsuppressed speckles and high side lobe artifacts.In this thesis, we propose to use a Bayesian approach to SAR imaging, which overcomes many drawbacks of classical methods and brings high resolution, more stable images and more accurate parameter estimation for target recognition.The proposed unifying approach is used for inverse problems in Mono-, Bi- and Multi-static SAR imaging, as well as for micromotion target imaging. Appropriate priors for modeling different target scenes in terms of target features enhancement during imaging are proposed. Fast and effective estimation methods with simple and hierarchical priors are developed. The problem of hyperparameter estimation is also handled in this Bayesian approach framework. Results on synthetic, experimental and real data demonstrate the effectiveness of the proposed approach.
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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.

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This thesis deals with regularization parameter selection methods in the context of Tikhonov-type regularization with Poisson distributed data, in particular the reconstruction of images, as well as with the identification of the volatility surface from observed option prices. In Part I we examine the choice of the regularization parameter when reconstructing an image, which is disturbed by Poisson noise, with Tikhonov-type regularization. This type of regularization is a generalization of the classical Tikhonov regularization in the Banach space setting and often called variational regularization. After a general consideration of Tikhonov-type regularization for data corrupted by Poisson noise, we examine the methods for choosing the regularization parameter numerically on the basis of two test images and real PET data. In Part II we consider the estimation of the volatility function from observed call option prices with the explicit formula which has been derived by Dupire using the Black-Scholes partial differential equation. The option prices are only available as discrete noisy observations so that the main difficulty is the ill-posedness of the numerical differentiation. Finite difference schemes, as regularization by discretization of the inverse and ill-posed problem, do not overcome these difficulties when they are used to evaluate the partial derivatives. Therefore we construct an alternative algorithm based on the weak formulation of the dual Black-Scholes partial differential equation and evaluate the performance of the finite difference schemes and the new algorithm for synthetic and real option prices.
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Books on the topic "Inverse imaging"

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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.

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F, Roach G., ed. Inverse problems and imaging. Harlow, Essex, England: Longman Scientific & Technical, 1991.

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Boerner, Wolfgang-M., Hans Brand, Leonard A. Cram, Dag T. Gjessing, Arthur K. Jordan, Wolfgang Keydel, Günther Schwierz, and Martin Vogel, eds. Inverse Methods in Electromagnetic Imaging. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5271-3.

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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.

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Boerner, Wolfgang-M., Hans Brand, Leonard A. Cram, Dag T. Gjessing, Arthur K. Jordan, Wolfgang Keydel, Günther Schwierz, and Martin Vogel, eds. Inverse Methods in Electromagnetic Imaging. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-010-9444-3.

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NATO Advanced Research Workshop on Inverse Methods in Electromagnetic Imaging (1983 Bad Windsheim, Germany). Inverse methods in electromagnetic imaging. Dordrecht, Holland: D. Reidel, 1985.

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Bryan, Kurt. An inverse problem in thermal imaging. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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Bryan, Kurt. An inverse problem in thermal imaging. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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Patrizia, Boccacci, ed. Introduction to inverse problems in imaging. Bristol, UK: Institute of Physics Pub., 1998.

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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.

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Book chapters on the topic "Inverse imaging"

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Wiskin, J., D. Borup, and S. Johnson. "Inverse Scattering Theory." In Acoustical Imaging, 53–59. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-3255-3_7.

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Wiskin, J., D. Borup, K. Callahan, Y. Parisky, J. Smith, M. P. André, and S. Johnson. "Inverse Scattering Results." In Acoustical Imaging, 61–68. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-3255-3_8.

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Mosegaard, Klaus, and Thomas Mejer Hansen. "Inverse Methods." In Integrated Imaging of the Earth, 7–27. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118929063.ch2.

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Colton, David, and Rainer Kress. "Inverse Scattering." In Handbook of Mathematical Methods in Imaging, 551–98. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-92920-0_13.

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Colton, David, and Rainer Kress. "Inverse Scattering." In Handbook of Mathematical Methods in Imaging, 649–700. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-0790-8_48.

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Langenberg, K. J., P. Fellinger, R. Marklein, P. Zanger, K. Mayer, and T. Kreutter. "Inverse Methods and Imaging." In The Evaluation of Materials and Structures by Quantitative Ultrasonics, 317–98. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-4315-5_19.

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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.

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Ramm, A. G. "Inverse Diffraction Problem." In Inverse Methods in Electromagnetic Imaging, 231–49. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-010-9444-3_15.

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Wiskin, J., D. T. Borup, S. A. Johnson, M. Berggren, T. Abbott, and R. Hanover. "Full-Wave, Non-Linear, Inverse Scattering." In Acoustical Imaging, 183–93. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/1-4020-5721-0_20.

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Burov, V. A., E. E. Kasatkina, and O. D. Rumiantseva. "Statistical Estimations in Inverse Scattering Problems." In Acoustical Imaging, 113–18. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4419-8772-3_18.

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Conference papers on the topic "Inverse imaging"

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Dolinsky, Margaret. "Inverse perspective." In Electronic Imaging 2006, edited by Andrew J. Woods, Neil A. Dodgson, John O. Merritt, Mark T. Bolas, and Ian E. McDowall. SPIE, 2006. http://dx.doi.org/10.1117/12.660340.

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Glasenapp, Carsten. "Shape measurement by inverse raytracing." In Unconventional Optical Imaging, edited by Corinne Fournier, Marc P. Georges, and Gabriel Popescu. SPIE, 2018. http://dx.doi.org/10.1117/12.2316349.

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Godfrey, Devon J., Richard J. Warp, and James T. Dobbins III. "Optimization of matrix inverse tomosynthesis." In Medical Imaging 2001, edited by Larry E. Antonuk and Martin J. Yaffe. SPIE, 2001. http://dx.doi.org/10.1117/12.430908.

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Schotland, John C. "Inverse scattering and quantum imaging." In Frontiers in Optics. Washington, D.C.: OSA, 2012. http://dx.doi.org/10.1364/fio.2012.fm3c.6.

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Ganapati, Vidya, Samarth Bhargava, and Eli Yablonovitch. "Inverse Electromagnetic Design for Imaging." In Adaptive Optics: Methods, Analysis and Applications. Washington, D.C.: OSA, 2013. http://dx.doi.org/10.1364/aopt.2013.jtu4a.16.

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Potthast, Roland. "On Ensemble Approaches to Inverse Scattering." In Mathematics in Imaging. Washington, D.C.: OSA, 2016. http://dx.doi.org/10.1364/math.2016.mm4h.1.

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Cheung, Shiufun, and Robert A. Ulichney. "Low-memory low-complexity inverse dithering." In Electronic Imaging '99, edited by Giordano B. Beretta and Reiner Eschbach. SPIE, 1998. http://dx.doi.org/10.1117/12.334598.

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Schouten, Theo E., and Egon L. van den Broek. "Inverse perspective transformation for video surveillance." In Electronic Imaging 2008, edited by Charles A. Bouman, Eric L. Miller, and Ilya Pollak. SPIE, 2008. http://dx.doi.org/10.1117/12.767236.

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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.

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Xiong, Zixiang, Michael T. Orchard, and Kannan Ramchandran. "Wavelet-based approach to inverse halftoning." In Electronic Imaging '97, edited by Giordano B. Beretta and Reiner Eschbach. SPIE, 1997. http://dx.doi.org/10.1117/12.271578.

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Reports on the topic "Inverse imaging"

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Wood, C. C. Electromagnetic inverse applications for functional brain imaging. Office of Scientific and Technical Information (OSTI), October 1997. http://dx.doi.org/10.2172/534510.

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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.

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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.

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Zittel, D., B. Brock, J. Littlejohn, and W. Patitz. Inverse-synthetic-aperture imaging of trees over a ground plane. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/186726.

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Hughett, P. An optimal constrained linear inverse method for magnetic source imaging. Office of Scientific and Technical Information (OSTI), September 1993. http://dx.doi.org/10.2172/10192344.

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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.

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Arellano, J., J. M. Hernandez, and J. Brase. Impulse radar imaging for dispersive concrete using inverse adaptive filtering techniques. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10117045.

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He, Yun. Multiscale Signal Processing and Shape Analysis for an Inverse Sar Imaging System. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada460126.

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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.

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Abstract:
This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
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Kadakia, Madhavi P. Optical Inverted Microscope Imaging System for Biological and Non-Biological Samples. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada499962.

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