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Do CNNs Solve the CT Inverse Problem? [PDF]
IEEE Trans Biomed Eng, 2021 Objective: This work examines the claim made in the literature that the inverse problem associated with image reconstruction in sparse-view computed tomography (CT) can be solved with a convolutional neural network (CNN).
Sidky EY, Lorente I, Brankov JG, Pan X.
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Minimal Geometric Deformation: the inverse problem [PDF]
European Physical Journal C: Particles and Fields, 2018 In this paper we show that any static and spherically symmetric anisotropic solution of the Einstein field equations can be thought as a system sourced by certain deformed isotropic system in the context of Minimal Geometric Deformation-decoupling ...
Ernesto Contreras
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Inverse Problem for a Curved Quantum Guide [PDF]
International Journal of Mathematics and Mathematical Sciences, 2012 We consider the Dirichlet Laplacian operator −Δ on a curved quantum guide in ℝ n(n=2,3) with an asymptotically straight reference curve. We give uniqueness results for the inverse problem associated to the reconstruction of the curvature by using either
Laure Cardoulis, Michel Cristofol
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Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers [PDF]
IEEE Open Journal of Signal ProcessingIn recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems.
Evan Scope Crafts, Umberto Villa
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Deep learning methods for inverse problems [PDF]
PeerJ Computer Science, 2022 In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three categories of Direct Mapping, Data Consistency Optimizer, and Deep Regularizer ...
Shima Kamyab +3 more
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Diffusion Posterior Sampling for General Noisy Inverse Problems [PDF]
International Conference on Learning Representations, 2022 Diffusion models have been recently studied as powerful generative inverse problem solvers, owing to their high quality reconstructions and the ease of combining existing iterative solvers.
Hyungjin Chung +4 more
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A sensitivity matrix based methodology for inverse problem formulation [PDF]
, 2020 We propose an algorithm to select parameter subset combinations that can be estimated using an ordinary least-squares (OLS) inverse problem formulation with a given data set.
A. Cintrón-Arias +3 more
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The inverse k-max combinatorial optimization problem [PDF]
Yugoslav Journal of Operations Research, 2023 Classical combinatorial optimization concerns finding a feasible subset of a ground set in order to optimize an objective function. We address in this article the inverse optimization problem with the k-max function. In other words, we attempt to perturb
Nhan Tran Hoai Ngoc +3 more
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Solving Inverse Problems with Latent Diffusion Models via Hard Data Consistency [PDF]
International Conference on Learning Representations, 2023 Diffusion models have recently emerged as powerful generative priors for solving inverse problems. However, training diffusion models in the pixel space are both data-intensive and computationally demanding, which restricts their applicability as priors ...
Bowen Song +5 more
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Consistency of Bayesian inference with Gaussian process priors in an elliptic inverse problem [PDF]
Inverse Problems, 2019 For O a bounded domain in Rd and a given smooth function g:O→R, we consider the statistical nonlinear inverse problem of recovering the conductivity f > 0 in the divergence form equation ∇⋅(f∇u)=gonO,u=0on∂O, from N discrete noisy point evaluations of ...
M. Giordano, Richard Nickl
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