Thematische Bibliographien / Imaging inverse problems / Zeitschriftenartikel

Zeitschriftenartikel zum Thema „Imaging inverse problems“

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Autor: Grafiati
Veröffentlicht am 7. Juli 2024
Zuletzt aktualisiert am 7. Juli 2024

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1

Ribes, Alejandro, und Francis Schmitt. „Linear inverse problems in imaging“. IEEE Signal Processing Magazine 25, Nr. 4 (Juli 2008): 84–99. http://dx.doi.org/10.1109/msp.2008.923099.

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2

Gilton, Davis, Gregory Ongie und 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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3

Oksanen, Lauri, und Mikko Salo. „Inverse problems in imaging and engineering science“. Mathematics in Engineering 2, Nr. 2 (2020): 287–89. http://dx.doi.org/10.3934/mine.2020014.

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4

Abubakar, Aria, und Maokun Li. „Electromagnetic Inverse Problems for Sensing and Imaging“. IEEE Antennas and Propagation Magazine 58, Nr. 2 (April 2016): 17. http://dx.doi.org/10.1109/map.2016.2520879.

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5

Kravchuk, Oleg, und Galyna Kriukova. „Regularization by Denoising for Inverse Problems in Imaging“. Mohyla Mathematical Journal 5 (28.12.2022): 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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6

Gilton, Davis, Gregory Ongie und 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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7

Bryan, Kurt, und Tanya Leise. „Impedance Imaging, Inverse Problems, and Harry Potter's Cloak“. SIAM Review 52, Nr. 2 (Januar 2010): 359–77. http://dx.doi.org/10.1137/090757873.

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8

Gilton, Davis, Greg Ongie und 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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9

Ongie, Gregory, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis und Rebecca Willett. „Deep Learning Techniques for Inverse Problems in Imaging“. IEEE Journal on Selected Areas in Information Theory 1, Nr. 1 (Mai 2020): 39–56. http://dx.doi.org/10.1109/jsait.2020.2991563.

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10

Habring, Andreas, und Martin Holler. „A Generative Variational Model for Inverse Problems in Imaging“. SIAM Journal on Mathematics of Data Science 4, Nr. 1 (März 2022): 306–35. http://dx.doi.org/10.1137/21m1414978.

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11

Ebrahimi, M., und E. R. Vrscay. „Regularization schemes involving self-similarity in imaging inverse problems“. Journal of Physics: Conference Series 124 (01.07.2008): 012021. http://dx.doi.org/10.1088/1742-6596/124/1/012021.

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12

Lewis D., John, Vanika Singhal und Angshul Majumdar. „Solving Inverse Problems in Imaging via Deep Dictionary Learning“. IEEE Access 7 (2019): 37039–49. http://dx.doi.org/10.1109/access.2018.2881492.

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13

Jin, Kyong Hwan, Michael T. McCann, Emmanuel Froustey und Michael Unser. „Deep Convolutional Neural Network for Inverse Problems in Imaging“. IEEE Transactions on Image Processing 26, Nr. 9 (September 2017): 4509–22. http://dx.doi.org/10.1109/tip.2017.2713099.

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14

Szasz, Teodora, Adrian Basarab und Denis Kouame. „Beamforming Through Regularized Inverse Problems in Ultrasound Medical Imaging“. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 63, Nr. 12 (Dezember 2016): 2031–44. http://dx.doi.org/10.1109/tuffc.2016.2608939.

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15

Tang, Junqi, Karen Egiazarian, Mohammad Golbabaee und Mike Davies. „The Practicality of Stochastic Optimization in Imaging Inverse Problems“. IEEE Transactions on Computational Imaging 6 (2020): 1471–85. http://dx.doi.org/10.1109/tci.2020.3032101.

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16

Kaasalainen1, Mikko, und Josef Ďurech. „Inverse problems of NEO photometry: Imaging the NEO population“. Proceedings of the International Astronomical Union 2, S236 (August 2006): 151–66. http://dx.doi.org/10.1017/s1743921307003195.

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AbstractThe physical properties of NEOs and other asteroids are mostly obtained with photometry. The resulting models describe the shapes, spin states, scattering properties and surface structure of the targets, and are the solutions of inverse problems involving comprehensive mathematical analysis. We review what can and cannot be obtained from photometric (and complementary) data, and how all this is done in practice. The role of photometry will become completely dominating with the advent of large-scale surveys capable of producing calibrated brightness data. Due to their quickly changing geometries with respect to the Earth, NEOs are the population that can be mapped the fastest.
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17

Rinkel, Jean, Jean Marie Polli und Eduardo X. Miqueles. „X-ray coherent diffraction imaging: Sequential inverse problems simulation“. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 912 (Dezember 2018): 43–47. http://dx.doi.org/10.1016/j.nima.2017.10.032.

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18

Ren, Kui, Rongting Zhang und Yimin Zhong. „Inverse transport problems in quantitative PAT for molecular imaging“. Inverse Problems 31, Nr. 12 (30.11.2015): 125012. http://dx.doi.org/10.1088/0266-5611/31/12/125012.

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19

Velikhovskyi, G. O., V. B. Molodkin, V. V. Lizunov, T. P. Vladimirova, S. V. Lizunova, Ya V. Vasylyk, M. P. Kulish, O. P. Dmytrenko, O. L. Pavlenko und Iu V. Davydova. „Solving of Direct and Inverse Scattering Problems for Heterogeneous Non-Crystalline Objects in Analyzer-Based Imaging“. METALLOFIZIKA I NOVEISHIE TEKHNOLOGII 41, Nr. 3 (26.05.2019): 375–88. http://dx.doi.org/10.15407/mfint.41.03.0375.

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20

Kwon, Kiwoon. „Uniqueness and Nonuniqueness in Inverse Problems for Elliptic Partial Differential Equations and Related Medical Imaging“. Advances in Mathematical Physics 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/908251.

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Unique determination issues about inverse problems for elliptic partial differential equations in divergence form are summarized and discussed. The inverse problems include medical imaging problems including electrical impedance tomography (EIT), diffuse optical tomography (DOT), and inverse scattering problem (ISP) which is an elliptic inverse problem closely related with DOT and EIT. If the coefficient inside the divergence is isotropic, many uniqueness results are known. However, it is known that inverse problem with anisotropic coefficients has many possible coefficients giving the same measured data for the inverse problem. For anisotropic coefficient with anomaly with or without jumps from known or unknown background, nonuniqueness of the inverse problems is discussed and the relation to cloaking or illusion of the anomaly is explained. The uniqueness and nonuniqueness issues are discussed firstly for EIT and secondly for ISP in similar arguments. Arguing the relation between source-to-detector map and Dirichlet-to-Neumann map in DOT and the uniqueness and nonuniqueness of DOT are also explained.
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21

González-Rodríguez, Pedro, Arnold D. Kim und Chrysoula Tsogka. „Corrigendum: Quantitative signal subspace imaging (2021 Inverse Problems 37 125006)“. Inverse Problems 38, Nr. 4 (23.02.2022): 049501. http://dx.doi.org/10.1088/1361-6420/ac509e.

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22

Tamburrino, A. „Monotonicity based imaging methods for elliptic and parabolic inverse problems“. Journal of Inverse and Ill-posed Problems 14, Nr. 6 (September 2006): 633–42. http://dx.doi.org/10.1515/156939406778474578.

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23

Skinner, G. K., und T. J. Ponman. „Inverse problems in X-ray and gamma-ray astronomical imaging“. Inverse Problems 11, Nr. 4 (01.08.1995): 655–76. http://dx.doi.org/10.1088/0266-5611/11/4/004.

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24

Dave, Akshat, Anil Kumar Vadathya, Ramana Subramanyam, Rahul Baburajan und Kaushik Mitra. „Solving Inverse Computational Imaging Problems Using Deep Pixel-Level Prior“. IEEE Transactions on Computational Imaging 5, Nr. 1 (März 2019): 37–51. http://dx.doi.org/10.1109/tci.2018.2882698.

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25

Schirrmacher, Franziska, Christian Riess und Thomas Kohler. „Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems“. IEEE Transactions on Computational Imaging 6 (2020): 503–17. http://dx.doi.org/10.1109/tci.2019.2956888.

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26

Raghavan, K. R., und A. E. Yagle. „Forward and inverse problems in elasticity imaging of soft tissues“. IEEE Transactions on Nuclear Science 41, Nr. 4 (1994): 1639–48. http://dx.doi.org/10.1109/23.322961.

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27

McCann, Michael T., Kyong Hwan Jin und Michael Unser. „Convolutional Neural Networks for Inverse Problems in Imaging: A Review“. IEEE Signal Processing Magazine 34, Nr. 6 (November 2017): 85–95. http://dx.doi.org/10.1109/msp.2017.2739299.

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28

Raj, Raghu G. „A hierarchical Bayesian-MAP approach to inverse problems in imaging“. Inverse Problems 32, Nr. 7 (12.05.2016): 075003. http://dx.doi.org/10.1088/0266-5611/32/7/075003.

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29

Hosseini, Mahdi S., und Konstantinos N. Plataniotis. „Finite Differences in Forward and Inverse Imaging Problems: MaxPol Design“. SIAM Journal on Imaging Sciences 10, Nr. 4 (Januar 2017): 1963–96. http://dx.doi.org/10.1137/17m1118452.

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30

Narnhofer, Dominik, Andreas Habring, Martin Holler und Thomas Pock. „Posterior-Variance–Based Error Quantification for Inverse Problems in Imaging“. SIAM Journal on Imaging Sciences 17, Nr. 1 (07.02.2024): 301–33. http://dx.doi.org/10.1137/23m1546129.

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31

Plessix, R. E. „A Helmholtz iterative solver for 3D seismic-imaging problems“. GEOPHYSICS 72, Nr. 5 (September 2007): SM185—SM194. http://dx.doi.org/10.1190/1.2738849.

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A preconditioned iterative solver for the 3D frequency-domain wave equation applied to seismic problems is evaluated. The preconditioner corresponds to an approximate inverse of a heavily damped wave equation deduced from the (undamped) wave equation. The approximate inverse is computed with one multigrid cycle. Numerical results show that the method is robust and that the number of iterations increases roughly linearly with frequency when the grid spacing is adapted to keep a constant number of discretization points per wavelength. To evaluate the relevance of this iterative solver, the number of floating-point operations required for two imaging problems are roughly evaluated. This rough estimate indicates that the time-domain migration approach is more than one order of magnitude faster. The full-wave-form tomography, based on a least-squares formulation and a scale separation approach, has the same complexity in both domains.
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32

Guzzi, Francesco, Alessandra Gianoncelli, Fulvio Billè, Sergio Carrato und George Kourousias. „Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy“. Life 13, Nr. 3 (23.02.2023): 629. http://dx.doi.org/10.3390/life13030629.

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Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems.
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33

Anjit, Thathamkulam Agamanan, Ria Benny, Philip Cherian und Palayyan Mythili. „NON-ITERATIVE MICROWAVE IMAGING SOLUTIONS FOR INVERSE PROBLEMS USING DEEP LEARNING“. Progress In Electromagnetics Research M 102 (2021): 53–63. http://dx.doi.org/10.2528/pierm21021304.

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34

Hyun, Chang Min, Seong Hyeon Baek, Mingyu Lee, Sung Min Lee und Jin Keun Seo. „Deep learning-based solvability of underdetermined inverse problems in medical imaging“. Medical Image Analysis 69 (April 2021): 101967. http://dx.doi.org/10.1016/j.media.2021.101967.

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35

Laves, Max-Heinrich, Malte Tölle, Alexander Schlaefer und Sandy Engelhardt. „Posterior temperature optimized Bayesian models for inverse problems in medical imaging“. Medical Image Analysis 78 (Mai 2022): 102382. http://dx.doi.org/10.1016/j.media.2022.102382.

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36

Lai, Ru-Yu, Kui Ren und Ting Zhou. „Inverse Transport and Diffusion Problems in Photoacoustic Imaging with Nonlinear Absorption“. SIAM Journal on Applied Mathematics 82, Nr. 2 (April 2022): 602–24. http://dx.doi.org/10.1137/21m1436178.

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37

Kim, Yong Y., und Rakesh K. Kapania. „Neural Networks for Inverse Problems in Damage Identification and Optical Imaging“. AIAA Journal 41, Nr. 4 (April 2003): 732–40. http://dx.doi.org/10.2514/2.2004.

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38

Agarwal, Krishna, und Xudong Chen. „Applicability of MUSIC-Type Imaging in Two-Dimensional Electromagnetic Inverse Problems“. IEEE Transactions on Antennas and Propagation 56, Nr. 10 (Oktober 2008): 3217–23. http://dx.doi.org/10.1109/tap.2008.929434.

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39

Oberai, Assad A., Nachiket H. Gokhale und Gonzalo R. Feij o. „Solution of inverse problems in elasticity imaging using the adjoint method“. Inverse Problems 19, Nr. 2 (06.02.2003): 297–313. http://dx.doi.org/10.1088/0266-5611/19/2/304.

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40

Goutsias, John I., und Jerry M. Mendel. „Inverse problems in two‐dimensional acoustic media: A linear imaging model“. Journal of the Acoustical Society of America 81, Nr. 5 (Mai 1987): 1471–85. http://dx.doi.org/10.1121/1.394500.

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41

Koo, Ja-Yong, und Peter T. Kim. „Sharp adaptation for spherical inverse problems with applications to medical imaging“. Journal of Multivariate Analysis 99, Nr. 2 (Februar 2008): 165–90. http://dx.doi.org/10.1016/j.jmva.2006.06.007.

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42

Scales, J. A., P. Docherty und A. Gersztenkorn. „Regularisation of nonlinear inverse problems: imaging the near-surface weathering layer“. Inverse Problems 6, Nr. 1 (01.02.1990): 115–31. http://dx.doi.org/10.1088/0266-5611/6/1/011.

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43

Anand, Christopher Kumar. „Robust Solvers for Inverse Imaging Problems Using Dense Single-Precision Hardware“. Journal of Mathematical Imaging and Vision 33, Nr. 1 (28.08.2008): 105–20. http://dx.doi.org/10.1007/s10851-008-0112-3.

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44

Chen, Zhiming, und Shiqi Zhou. „A direct imaging method for half-space inverse elastic scattering problems“. Inverse Problems 35, Nr. 7 (25.06.2019): 075004. http://dx.doi.org/10.1088/1361-6420/ab08ab.

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45

Repetti, Audrey, Marcelo Pereyra und Yves Wiaux. „Scalable Bayesian Uncertainty Quantification in Imaging Inverse Problems via Convex Optimization“. SIAM Journal on Imaging Sciences 12, Nr. 1 (Januar 2019): 87–118. http://dx.doi.org/10.1137/18m1173629.

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46

Kim, Taewoo, Renjie Zhou, Lynford L. Goddard und Gabriel Popescu. „Solving inverse scattering problems in biological samples by quantitative phase imaging“. Laser & Photonics Reviews 10, Nr. 1 (16.12.2015): 13–39. http://dx.doi.org/10.1002/lpor.201400467.

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47

Evangelista, Davide, Elena Morotti, Elena Loli Piccolomini und James Nagy. „Ambiguity in Solving Imaging Inverse Problems with Deep-Learning-Based Operators“. Journal of Imaging 9, Nr. 7 (30.06.2023): 133. http://dx.doi.org/10.3390/jimaging9070133.

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In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well known that image deblurring is mathematically modeled as an ill-posed inverse problem and its solution is difficult to approximate when noise affects the data. Really, one limitation of neural networks for deblurring is their sensitivity to noise and other perturbations, which can lead to instability and produce poor reconstructions. In addition, networks do not necessarily take into account the numerical formulation of the underlying imaging problem when trained end-to-end. In this paper, we propose some strategies to improve stability without losing too much accuracy to deblur images with deep-learning-based methods. First, we suggest a very small neural architecture, which reduces the execution time for training, satisfying a green AI need, and does not extremely amplify noise in the computed image. Second, we introduce a unified framework where a pre-processing step balances the lack of stability of the following neural-network-based step. Two different pre-processors are presented. The former implements a strong parameter-free denoiser, and the latter is a variational-model-based regularized formulation of the latent imaging problem. This framework is also formally characterized by mathematical analysis. Numerical experiments are performed to verify the accuracy and stability of the proposed approaches for image deblurring when unknown or not-quantified noise is present; the results confirm that they improve the network stability with respect to noise. In particular, the model-based framework represents the most reliable trade-off between visual precision and robustness.
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48

Hou, Songming, Yihong Jiang und Yuan Cheng. „Direct and Inverse Scattering Problems for Domains with Multiple Corners“. International Journal of Partial Differential Equations 2015 (26.01.2015): 1–9. http://dx.doi.org/10.1155/2015/968529.

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We proposed numerical methods for solving the direct and inverse scattering problems for domains with multiple corners. Both the near field and far field cases are considered. For the forward problem, the challenges of logarithmic singularity from Green’s functions and corner singularity are both taken care of. For the inverse problem, an efficient and robust direct imaging method is proposed. Multiple frequency data are combined to capture details while not losing robustness.
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49

Ji, Liya, Zhefan Rao, Sinno Jialin Pan, Chenyang Lei und Qifeng Chen. „A Diffusion Model with State Estimation for Degradation-Blind Inverse Imaging“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 3 (24.03.2024): 2471–79. http://dx.doi.org/10.1609/aaai.v38i3.28023.

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Solving the task of inverse imaging problems can restore unknown clean images from input measurements that have incomplete information. Utilizing powerful generative models, such as denoising diffusion models, could better tackle the ill-posed issues of inverse problems with the distribution prior of the unknown clean images. We propose a learnable state-estimator-based diffusion model to incorporate the measurements into the reconstruction process. Our method makes efficient use of the pre-trained diffusion models with computational feasibility compared to the conditional diffusion models, which need to be trained from scratch. In addition, our pipeline does not require explicit knowledge of the image degradation operator or make the assumption of its form, unlike many other works that use the pre-trained diffusion models at the test time. The experiments on three typical inverse imaging problems (both linear and non-linear), inpainting, deblurring, and JPEG compression restoration, have comparable results with the state-of-the-art methods.
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50

Denker, Alexander, Maximilian Schmidt, Johannes Leuschner und Peter Maass. „Conditional Invertible Neural Networks for Medical Imaging“. Journal of Imaging 7, Nr. 11 (17.11.2021): 243. http://dx.doi.org/10.3390/jimaging7110243.

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Over recent years, deep learning methods have become an increasingly popular choice for solving tasks from the field of inverse problems. Many of these new data-driven methods have produced impressive results, although most only give point estimates for the reconstruction. However, especially in the analysis of ill-posed inverse problems, the study of uncertainties is essential. In our work, we apply generative flow-based models based on invertible neural networks to two challenging medical imaging tasks, i.e., low-dose computed tomography and accelerated medical resonance imaging. We test different architectures of invertible neural networks and provide extensive ablation studies. In most applications, a standard Gaussian is used as the base distribution for a flow-based model. Our results show that the choice of a radial distribution can improve the quality of reconstructions.
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