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SIAM J. Imaging Sci.最新文献

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Imaging a moving point source from multi-frequency data measured at one and sparse observation directions (part I): far-field case 从一个和稀疏观测方向上测量的多频数据成像移动点源(第一部分):远场情况
SIAM J. Imaging Sci. Pub Date : 2022-12-29 DOI: 10.48550/arXiv.2212.14236
Hongxia Guo, G. Hu, Guanqiu Ma
{"title":"Imaging a moving point source from multi-frequency data measured at one and sparse observation directions (part I): far-field case","authors":"Hongxia Guo, G. Hu, Guanqiu Ma","doi":"10.48550/arXiv.2212.14236","DOIUrl":"https://doi.org/10.48550/arXiv.2212.14236","url":null,"abstract":"We propose a multi-frequency algorithm for imaging the trajectory of a moving point source from one and sparse far-field observation directions in the frequency domain. The starting and terminal time points of the moving source are both supposed to be known. We introduce the concept of observable directions (angles) in the far-field region and derive all observable directions (angles) for straight and circular motions. At an observable direction, it is verified that the smallest trip containing the trajectory and perpendicular to the direction can be imaged, provided the orbit function possesses a certain monotonical property. Without the monotonicity one can only expect to recover a thinner strip. The far-field data measured at sparse observable directions can be used to recover the $Theta$-convex domain of the trajectory. Both two- and three-dimensional numerical examples are implemented to show effectiveness and feasibility of the approach.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133752823","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: 0
Using Decoupled Features for Photorealistic Style Transfer 使用解耦特征的逼真风格转移
SIAM J. Imaging Sci. Pub Date : 2022-12-05 DOI: 10.1137/22m1512491
Trevor Canham, Adrián Martín Fernández, M. Bertalmío, J. Portilla
{"title":"Using Decoupled Features for Photorealistic Style Transfer","authors":"Trevor Canham, Adrián Martín Fernández, M. Bertalmío, J. Portilla","doi":"10.1137/22m1512491","DOIUrl":"https://doi.org/10.1137/22m1512491","url":null,"abstract":"In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130377558","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
A Quantum Information-Based Refoundation of Color Perception Concepts 基于量子信息的色彩感知概念重构
SIAM J. Imaging Sci. Pub Date : 2022-11-21 DOI: 10.1137/22m1476071
M. Berthier, Nicoletta Prencipe, E. Provenzi
{"title":"A Quantum Information-Based Refoundation of Color Perception Concepts","authors":"M. Berthier, Nicoletta Prencipe, E. Provenzi","doi":"10.1137/22m1476071","DOIUrl":"https://doi.org/10.1137/22m1476071","url":null,"abstract":"","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114409671","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: 3
An Algorithm to Compute Any Simple $k$-gon of a Maximum Area or Perimeter Inscribed in a Region of Interest 一种计算感兴趣区域内最大面积或周长的任何简单$k$-gon的算法
SIAM J. Imaging Sci. Pub Date : 2022-11-14 DOI: 10.2139/ssrn.4038040
R. Molano, M. Ávila, J. Sancho, P. Rodríguez, A. Caro
{"title":"An Algorithm to Compute Any Simple $k$-gon of a Maximum Area or Perimeter Inscribed in a Region of Interest","authors":"R. Molano, M. Ávila, J. Sancho, P. Rodríguez, A. Caro","doi":"10.2139/ssrn.4038040","DOIUrl":"https://doi.org/10.2139/ssrn.4038040","url":null,"abstract":"","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130772137","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: 0
The linear sampling method for random sources 随机源的线性抽样方法
SIAM J. Imaging Sci. Pub Date : 2022-10-27 DOI: 10.48550/arXiv.2210.15560
J. Garnier, H. Haddar, Hadrien Montanelli
{"title":"The linear sampling method for random sources","authors":"J. Garnier, H. Haddar, Hadrien Montanelli","doi":"10.48550/arXiv.2210.15560","DOIUrl":"https://doi.org/10.48550/arXiv.2210.15560","url":null,"abstract":"We present an extension of the linear sampling method for solving the sound-soft inverse acoustic scattering problem with randomly distributed point sources. The theoretical justification of our sampling method is based on the Helmholtz--Kirchhoff identity, the cross-correlation between measurements, and the volume and imaginary near-field operators, which we introduce and analyze. Implementations in MATLAB using boundary elements, the SVD, Tikhonov regularization, and Morozov's discrepancy principle are also discussed. We demonstrate the robustness and accuracy of our algorithms with several numerical experiments in two dimensions.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127852971","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: 0
Convergence Analysis of Volumetric Stretch Energy Minimization and its Associated Optimal Mass Transport 体积拉伸能量最小化及其最优质量输运的收敛性分析
SIAM J. Imaging Sci. Pub Date : 2022-10-18 DOI: 10.48550/arXiv.2210.09654
Tsung-Ming Huang, Wei-Hung Liao, Wen-Wei Lin, M. Yueh, S. Yau
{"title":"Convergence Analysis of Volumetric Stretch Energy Minimization and its Associated Optimal Mass Transport","authors":"Tsung-Ming Huang, Wei-Hung Liao, Wen-Wei Lin, M. Yueh, S. Yau","doi":"10.48550/arXiv.2210.09654","DOIUrl":"https://doi.org/10.48550/arXiv.2210.09654","url":null,"abstract":"The volumetric stretch energy has been widely applied to the computation of volume-/mass-preserving parameterizations of simply connected tetrahedral mesh models. However, this approach still lacks theoretical support. In this paper, we provide the theoretical foundation for volumetric stretch energy minimization (VSEM) to compute volume-/mass-preserving parameterizations. In addition, we develop an associated efficient VSEM algorithm with guaranteed asymptotic R-linear convergence. Furthermore, based on the VSEM algorithm, we propose a projected gradient method for the computation of the volume/mass-preserving optimal mass transport map with a guaranteed convergence rate of $mathcal{O}(1/m)$, and combined with Nesterov-based acceleration, the guaranteed convergence rate becomes $mathcal{O}(1/m^2)$. Numerical experiments are presented to justify the theoretical convergence behavior for various examples drawn from known benchmark models. Moreover, these numerical experiments show the effectiveness and accuracy of the proposed algorithm, particularly in the processing of 3D medical MRI brain images.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"23 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114034365","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: 0
Provable Phase Retrieval with Mirror Descent 可证明的相位反演与镜像下降
SIAM J. Imaging Sci. Pub Date : 2022-10-17 DOI: 10.48550/arXiv.2210.09248
Jean-Jacques-Narcisse Godeme, M. Fadili, Xavier Buet, M. Zerrad, M. Lequime, C. Amra
{"title":"Provable Phase Retrieval with Mirror Descent","authors":"Jean-Jacques-Narcisse Godeme, M. Fadili, Xavier Buet, M. Zerrad, M. Lequime, C. Amra","doi":"10.48550/arXiv.2210.09248","DOIUrl":"https://doi.org/10.48550/arXiv.2210.09248","url":null,"abstract":"In this paper, we consider the problem of phase retrieval, which consists of recovering an $n$-dimensional real vector from the magnitude of its $m$ linear measurements. We propose a mirror descent (or Bregman gradient descent) algorithm based on a wisely chosen Bregman divergence, hence allowing to remove the classical global Lipschitz continuity requirement on the gradient of the non-convex phase retrieval objective to be minimized. We apply the mirror descent for two random measurements: the iid standard Gaussian and those obtained by multiple structured illuminations through Coded Diffraction Patterns (CDP). For the Gaussian case, we show that when the number of measurements $m$ is large enough, then with high probability, for almost all initializers, the algorithm recovers the original vector up to a global sign change. For both measurements, the mirror descent exhibits a local linear convergence behaviour with a dimension-independent convergence rate. Our theoretical results are finally illustrated with various numerical experiments, including an application to the reconstruction of images in precision optics.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133919843","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: 0
Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising 高斯斑块混合模型引导的低秩协方差矩阵最小化图像去噪
SIAM J. Imaging Sci. Pub Date : 2022-10-06 DOI: 10.1137/21m1454262
Jing Guo, Yu Guo, Qiyu Jin, M. K. Ng, Shuping Wang
{"title":"Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising","authors":"Jing Guo, Yu Guo, Qiyu Jin, M. K. Ng, Shuping Wang","doi":"10.1137/21m1454262","DOIUrl":"https://doi.org/10.1137/21m1454262","url":null,"abstract":"","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132408296","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
Regularizing Orientation Estimation in Cryogenic Electron Microscopy Three-Dimensional Map Refinement through Measure-Based Lifting over Riemannian Manifolds 黎曼流形上基于测度提升的低温电子显微镜三维图精细中的正则化方向估计
SIAM J. Imaging Sci. Pub Date : 2022-09-07 DOI: 10.1137/22M1520773
W. Diepeveen, J. Lellmann, O. Öktem, C. Schönlieb
{"title":"Regularizing Orientation Estimation in Cryogenic Electron Microscopy Three-Dimensional Map Refinement through Measure-Based Lifting over Riemannian Manifolds","authors":"W. Diepeveen, J. Lellmann, O. Öktem, C. Schönlieb","doi":"10.1137/22M1520773","DOIUrl":"https://doi.org/10.1137/22M1520773","url":null,"abstract":"Motivated by the trade-off between noise-robustness and data-consistency for joint 3D map reconstruction and rotation estimation in single particle cryogenic-electron microscopy (Cryo-EM), we propose ellipsoidal support lifting (ESL), a measure-based lifting scheme for regularising and approximating the global minimiser of a smooth function over a Riemannian manifold. Under a uniqueness assumption on the minimiser we show several theoretical results, in particular well-posedness of the method and an error bound due to the induced bias with respect to the global minimiser. Additionally, we use the developed theory to integrate the measure-based lifting scheme into an alternating update method for joint homogeneous 3D map reconstruction and rotation estimation, where typically tens of thousands of manifold-valued minimisation problems have to be solved and where regularisation is necessary because of the high noise levels in the data. The joint recovery method is used to test both the theoretical predictions and algorithmic performance through numerical experiments with Cryo-EM data. In particular, the induced bias due to the regularising effect of ESL empirically estimates better rotations, i.e., rotations closer to the ground truth, than global optimisation would.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"454 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116061876","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: 2
An Accelerated Level-Set Method for Inverse Scattering Problems 逆散射问题的加速水平集方法
SIAM J. Imaging Sci. Pub Date : 2022-09-01 DOI: 10.1137/21m1457783
Lorenzo Audibert, H. Haddar, Xiaoli Liu
{"title":"An Accelerated Level-Set Method for Inverse Scattering Problems","authors":"Lorenzo Audibert, H. Haddar, Xiaoli Liu","doi":"10.1137/21m1457783","DOIUrl":"https://doi.org/10.1137/21m1457783","url":null,"abstract":"We propose a rapid and robust iterative algorithm to solve inverse acoustic scattering problems formulated as a PDE constrained shape optimization problem. We use a level-set method to represent the obstacle geometry and propose a new scheme for updating the geometry based on an adaptation of accelerated gradient descent methods. The resulting algorithm aims at reducing the number of iterations and improving the accuracy of reconstructions. To cope with regularization issues, we pro-pose a smoothing to the shape gradient using a single layer potential associated with ik where k is the wave number. Numerical experiments are given for several data types (full aperture, backscattering, phaseless, multiple frequencies) and show that our method outperforms a non accelerated approach in terms of convergence speed, accuracy and sensitivity to initial guesses.","PeriodicalId":185319,"journal":{"name":"SIAM J. Imaging Sci.","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124880287","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: 3
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