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Compressed sensing (CS) is a promising approach to reduce the number of measurements in photoacoustic tomography (PAT) while preserving high spatial resolution. This allows to increase the measurement speed and to reduce system costs.…

The success of the compressed sensing paradigm has shown that a substantial reduction in sampling and storage complexity can be achieved in certain linear and non-adaptive estimation problems. It is therefore an advisable strategy for…

信息论 · 计算机科学 2014-08-27 Peter Jung , Philipp Walk

Reverberation mapping offers one of the best techniques for studying the inner regions of QSOs. It is based on cross-correlating continuum and emission-line light curves. New time-resolved optical surveys will produce well sampled light…

宇宙学与河外天体物理 · 物理学 2015-06-05 S. Fine , T. Shanks , S. M. Croom , P. Green , B. C. Kelly , E. Berge , R. Chornock , W. S. Burgett , E. A. Magnier , P. A. Price

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. However, most works focus on solving simple linear…

机器学习 · 统计学 2025-10-06 Hyungjin Chung , Jeongsol Kim , Michael T. Mccann , Marc L. Klasky , Jong Chul Ye

The single-scatter approximation is fundamental in many tomographic imaging problems including x-ray scatter imaging and optical scatter imaging for certain media. In all cases, noisy measurements are affected by both local scatter events…

图像与视频处理 · 电气工程与系统科学 2021-04-21 Michael R. Walker , Joseph A. O'Sullivan

The replica method is a non-rigorous but well-known technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method, under the assumption of replica symmetry, to…

信息论 · 计算机科学 2015-01-20 Sundeep Rangan , Alyson K. Fletcher , Vivek K Goyal

We present Wideband Back-Projection Diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data. This framework produces highly accurate…

机器学习 · 计算机科学 2025-05-20 Borong Zhang , Martín Guerra , Qin Li , Leonardo Zepeda-Núñez

Sliced inverse regression (SIR) is a popular sufficient dimension reduction method that identifies a few linear transformations of the covariates without losing regression information with the response. In high-dimensional settings, SIR can…

统计方法学 · 统计学 2025-12-04 Linh H. Nghiem , Francis. K. C. Hui , Samuel Muller , A. H. Welsh

Time harmonic inverse scattering using accurate forward models is often computationally expensive. On the other hand, the use of computationally efficient solvers, such as the Born approximation, may fail if the targets do not satisfy the…

计算物理 · 物理学 2019-07-05 Jari P. Kaipio , Tomi Huttunen , Teemu Luostari , Timo Lähivaara , Peter B. Monk

This paper proposes a method for detecting multiple scatterers (targets) in the elevation direction for synthetic aperture radar (SAR) tomography. The proposed method can resolve closely spaced targets through a twostep procedure. In the…

信息论 · 计算机科学 2022-04-11 Ahmad Naghavi , Mohammad Sadegh Fazel , Mojtaba Beheshti , Ehsan Yazdian

A 3-D inverse medium problem in the frequency domain is considered. Another name for this problem is Coefficient Inverse Problem. The goal is to reconstruct spatially distributed dielectric constants from scattering data. Potential…

数值分析 · 数学 2016-05-23 Michael V. Klibanov , Hui Liu , Loc H. Nguyen

We introduce and compare new compression approaches to obtain regularized solutions of large linear systems which are commonly encountered in large scale inverse problems. We first describe how to approximate matrix vector operations with a…

数值分析 · 数学 2016-08-12 Sergey Voronin , Dylan Mikesell , Guust Nolet

We are concerned with the inverse scattering problem of extracting the geometric structures of an unknown/inaccessible inhomogeneous medium by using the corresponding acoustic far-field measurement. Using the intrinsic geometric properties…

偏微分方程分析 · 数学 2017-06-15 Jingzhi Li , Xiaofei Li , Hongyu Liu

From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain. Most of…

计算机视觉与模式识别 · 计算机科学 2014-05-01 Jian Zhang , Chen Zhao , Debin Zhao , Wen Gao

We study an inverse scattering problem for a generic hyperbolic system of equations with an unknown coefficient called the reflectivity. The solution of the system models waves (sound, electromagnetic or elastic), and the reflectivity…

The recent development of scintillation crystals combined with $\gamma$-rays sources opens the way to an imaging concept based on Compton scattering, namely Compton scattering tomography (CST). The associated inverse problem rises many…

数值分析 · 数学 2023-02-22 Janek Gödeke , Gaël Rigaud

A heuristic procedure based on novel recursive formulation of sinusoid (RFS) and on regression with predictive least-squares (LS) enables to decompose both uniformly and nonuniformly sampled 1-d signals into a sparse set of sinusoids (SSS).…

信息论 · 计算机科学 2017-04-13 Ivan Maric

A compressed sensing scheme for near-field imaging of corrugations of relative sparse Fourier components is proposed. The scheme employs random sparse measurement of near field to recover the angular spectrum of the scattered field. It is…

光学 · 物理学 2015-05-30 Albert Fannjiang , Hsiao-Chieh Tseng

Computational time reversal imaging can be used to locate the position of multiple scatterers in a known background medium. The current methods for computational time reversal imaging are based on the null subspace projection operator,…

数据分析、统计与概率 · 物理学 2009-06-16 M. Andrecut

Due to manufacturing defects or wear and tear, industrial components may have uncertainties. In order to evaluate the performance of machined components, it is crucial to quantify the uncertainty of the scattering surface. This brings up an…

数值分析 · 数学 2025-04-28 Yi Wang , Lei Lin , Junliang Lv