SIAM J. Imaging Sci.最新文献_第6页

Robust Tensor Completion: Equivalent Surrogates, Error Bounds, and Algorithms 鲁棒张量补全:等效代理，误差边界和算法

SIAM J. Imaging Sci. Pub Date : 2022-05-24 DOI: 10.1137/21m1429539

Xueying Zhao, Minru Bai, Defeng Sun, Libin Zheng

引用次数: 7

On Assignment Problems Related to Gromov-Wasserstein Distances on the Real Line 实线上与Gromov-Wasserstein距离有关的分配问题

SIAM J. Imaging Sci. Pub Date : 2022-05-18 DOI: 10.48550/arXiv.2205.09006

Robert Beinert, Cosmas Heiß, G. Steidl

引用次数: 9

Variational Rician Noise Removal via Splitting on Spheres 基于球面分裂的变分时域噪声去除

SIAM J. Imaging Sci. Pub Date : 2022-05-16 DOI: 10.1137/21m1452792

Zhifang Liu, Huibin Chang, Y. Duan

引用次数: 2

A Nonlocal Graph-PDE and Higher-Order Geometric Integration for Image Labeling 图像标注的非局部图pde和高阶几何积分

SIAM J. Imaging Sci. Pub Date : 2022-05-09 DOI: 10.48550/arXiv.2205.03991

Dmitrij Sitenko, Bastian Boll, C. Schnörr

引用次数: 1

PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent 即插即用梯度下降的学习正则化梯度

SIAM J. Imaging Sci. Pub Date : 2022-04-29 DOI: 10.1137/22m1490843

Rita Fermanian, Mikael Le Pendu, C. Guillemot

{"title":"PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent","authors":"Rita Fermanian, Mikael Le Pendu, C. Guillemot","doi":"10.1137/22m1490843","DOIUrl":"https://doi.org/10.1137/22m1490843","url":null,"abstract":"The Plug-and-Play (PnP) framework makes it possible to integrate advanced image denoising priors into optimization algorithms, to efficiently solve a variety of image restoration tasks generally formulated as Maximum A Posteriori (MAP) estimation problems. The Plug-and-Play alternating direction method of multipliers (ADMM) and the Regularization by Denoising (RED) algorithms are two examples of such methods that made a breakthrough in image restoration. However, while the former method only applies to proximal algorithms, it has recently been shown that there exists no regularization that explains the RED algorithm when the denoisers lack Jacobian symmetry, which happen to be the case of most practical denoisers. To the best of our knowledge, there exists no method for training a network that directly represents the gradient of a regularizer, which can be directly used in Plug-and-Play gradient-based algorithms. We show that it is possible to train a network directly modeling the gradient of a MAP regularizer while jointly training the corresponding MAP denoiser. We use this network in gradient-based optimization methods and obtain better results comparing to other generic Plug-and-Play approaches. We also show that the regularizer can be used as a pre-trained network for unrolled gradient descent. Lastly, we show that the resulting denoiser allows for a better convergence of the Plug-and-Play ADMM.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128568950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Displacement and Pressure Reconstruction from Magnetic Resonance Elastography Images: Application to an In Silico Brain Model 磁共振弹性成像的位移和压力重建:在计算机脑模型中的应用

SIAM J. Imaging Sci. Pub Date : 2022-04-26 DOI: 10.1137/22m149363x

F. Galarce, K. Tabelow, J. Polzehl, Christos Panagiotis Papanikas, V. Vavourakis, Ledia Lilaj, I. Sack, A. Caiazzo

{"title":"Displacement and Pressure Reconstruction from Magnetic Resonance Elastography Images: Application to an In Silico Brain Model","authors":"F. Galarce, K. Tabelow, J. Polzehl, Christos Panagiotis Papanikas, V. Vavourakis, Ledia Lilaj, I. Sack, A. Caiazzo","doi":"10.1137/22m149363x","DOIUrl":"https://doi.org/10.1137/22m149363x","url":null,"abstract":"Magnetic resonance elastography is a motion-sensitive image modality that allows to measure in vivo tissue displacement fields in response to mechanical excitations. This paper investigates a data assimilation approach for reconstructing tissue displacement and pressure fields in an in silico brain model from partial elastography data. The data assimilation is based on a parametrized-background data weak methodology, in which the state of the physical system -- tissue displacements and pressure fields -- is reconstructed from the available data assuming an underlying poroelastic biomechanics model. For this purpose, a physics-informed manifold is built by sampling the space of parameters describing the tissue model close to their physiological ranges to simulate the corresponding poroelastic problem, and computing a reduced basis via Proper Orthogonal Decomposition. Displacements and pressure reconstruction is sought in a reduced space after solving a minimization problem that encompasses both the structure of the reduced-order model and the available measurements. The proposed pipeline is validated using synthetic data obtained after simulating the poroelastic mechanics of a physiological brain. The numerical experiments demonstrate that the framework can exhibit accurate joint reconstructions of both displacement and pressure fields. The methodology can be formulated for an arbitrary resolution of available displacement data from pertinent images.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector 利用平面定向敏感探测器学习光声层析成像中的不可见性

SIAM J. Imaging Sci. Pub Date : 2022-04-21 DOI: 10.48550/arXiv.2204.10001

Bolin Pan, M. Betcke

{"title":"On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector","authors":"Bolin Pan, M. Betcke","doi":"10.48550/arXiv.2204.10001","DOIUrl":"https://doi.org/10.48550/arXiv.2204.10001","url":null,"abstract":"In photoacoustic tomography (PAT) with flat sensor, we routinely encounter two types of limited data. The first is due to using a finite sensor and is especially perceptible if the region of interest is large relative to the sensor or located farther away from the sensor. In this paper, we focus on the second type caused by a varying sensitivity of the sensor to the incoming wavefront direction which can be modelled as binary i.e. by a cone of sensitivity. Such visibility conditions result, in the Fourier domain, in a restriction of both the image and the data to a bow-tie, akin to the one corresponding to the range of the forward operator. The visible wavefrontsets in image and data domains, are related by the wavefront direction mapping. We adapt the wedge restricted Curvelet decomposition, we previously proposed for the representation of the full PAT data, to separate the visible and invisible wavefronts in the image. We optimally combine fast approximate operators with tailored deep neural network architectures into efficient learned reconstruction methods which perform reconstruction of the visible coefficients and the invisible coefficients are learned from a training set of similar data.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126949473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Utilizing Variational Autoencoders in the Bayesian Inverse Problem of Photoacoustic Tomography 变分自编码器在光声层析成像贝叶斯反问题中的应用

SIAM J. Imaging Sci. Pub Date : 2022-04-13 DOI: 10.1137/22m1489897

Teemu Sahlström, T. Tarvainen

引用次数: 4

Efficient Boosted DC Algorithm for Nonconvex Image Restoration with Rician Noise 含噪非凸图像恢复的高效增强DC算法

SIAM J. Imaging Sci. Pub Date : 2022-04-04 DOI: 10.1137/21m1421660

Tingting Wu, Xiaoyu Gu, Zeyu Li, Zhi Li, Jianwei Niu, T. Zeng

引用次数: 5

Compressive Imaging Through Optical Fiber with Partial Speckle Scanning 部分散斑扫描光纤压缩成像

SIAM J. Imaging Sci. Pub Date : 2022-04-04 DOI: 10.1137/21m1407586

S. Guérit, Siddharth Sivankutty, John Lee, H. Rigneault, L. Jacques

引用次数: 4