SIAM Journal on Imaging Sciences最新文献_第8页

(boldsymbol{L_1-beta L_q}) Minimization for Signal and Image Recovery (boldsymbol{L_1-beta L_q}) 最小化的信号和图像恢复

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-10-11 DOI: 10.1137/22m1525363

Limei Huo, Wengu Chen, Huanmin Ge, Michael K. Ng

引用次数: 0

Subaperture-Based Digital Aberration Correction for Optical Coherence Tomography: A Novel Mathematical Approach 基于子孔径的光学相干层析成像数字像差校正:一种新的数学方法

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-10-11 DOI: 10.1137/22m1543240

Simon Hubmer, Ekaterina Sherina, Ronny Ramlau, Michael Pircher, Rainer Leitgeb

引用次数: 0

Short Communication: Localized Adversarial Artifacts for Compressed Sensing MRI 短通信:压缩感知MRI的局部对抗伪影

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-10-10 DOI: 10.1137/22m1503221

Rima Alaifari, Giovanni S. Alberti, Tandri Gauksson

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

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-09-08 DOI: 10.1137/22m1528756

Tsung-Ming Huang, Wei-Hung Liao, Wen-Wei Lin, Mei-Heng Yueh, Shing-Tung Yau

引用次数: 0

Convexification Numerical Method for a Coefficient Inverse Problem for the Riemannian Radiative Transfer Equation 黎曼辐射传递方程系数反问题的凸化数值解法

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-29 DOI: 10.1137/23m1565449

Michael V. Klibanov, Jingzhi Li, Loc H. Nguyen, Vladimir Romanov, Zhipeng Yang

引用次数: 1

Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper 图像去噪:深度学习革命和超越-一篇调查论文

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-24 DOI: 10.1137/23m1545859

Michael Elad, Bahjat Kawar, Gregory Vaksman

{"title":"Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper","authors":"Michael Elad, Bahjat Kawar, Gregory Vaksman","doi":"10.1137/23m1545859","DOIUrl":"https://doi.org/10.1137/23m1545859","url":null,"abstract":"Image denoising—removal of additive white Gaussian noise from an image—is one of the oldest and most studied problems in image processing. Extensive work over several decades has led to thousands of papers on this subject, and to many well-performing algorithms for this task. Indeed, 10 years ago, these achievements led some researchers to suspect that “Denoising is Dead,” in the sense that all that can be achieved in this domain has already been obtained. However, this turned out to be far from the truth, with the penetration of deep learning (DL) into the realm of image processing. The era of DL brought a revolution to image denoising, both by taking the lead in today’s ability for noise suppression in images, and by broadening the scope of denoising problems being treated. Our paper starts by describing this evolution, highlighting in particular the tension and synergy that exist between classical approaches and modern artificial intelligence (AI) alternatives in design of image denoisers. The recent transitions in the field of image denoising go far beyond the ability to design better denoisers. In the second part of this paper we focus on recently discovered abilities and prospects of image denoisers. We expose the possibility of using image denoisers for service of other problems, such as regularizing general inverse problems and serving as the prime engine in diffusion-based image synthesis. We also unveil the (strange?) idea that denoising and other inverse problems might not have a unique solution, as common algorithms would have us believe. Instead, we describe constructive ways to produce randomized and diverse high perceptual quality results for inverse problems, all fueled by the progress that DL brought to image denoising. This is a survey paper, and its prime goal is to provide a broad view of the history of the field of image denoising and closely related topics in image processing. Our aim is to give a better context to recent discoveries, and to the influence of the AI revolution in our domain.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134983002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Linear Sampling Method for Random Sources 随机源的线性抽样方法

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-23 DOI: 10.1137/22m1531336

Josselin Garnier, Houssem Haddar, Hadrien Montanelli

引用次数: 0

Imaging a Moving Point Source from Multifrequency Data Measured at One and Sparse Observation Directions (Part I): Far-Field Case 从一个和稀疏观测方向测量的多频数据成像移动点源(第一部分):远场情况

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-17 DOI: 10.1137/23m1545045

Hongxia Guo, Guanghui Hu, Guanqiu Ma

{"title":"Imaging a Moving Point Source from Multifrequency Data Measured at One and Sparse Observation Directions (Part I): Far-Field Case","authors":"Hongxia Guo, Guanghui Hu, Guanqiu Ma","doi":"10.1137/23m1545045","DOIUrl":"https://doi.org/10.1137/23m1545045","url":null,"abstract":"We propose a multifrequency algorithm for recovering partial information on 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. The existence of nonobservable directions makes this paper much different from inverse stationary source problems. At an observable direction, it is verified that the smallest strip containing the trajectory and perpendicular to the direction can be imaged, provided the angle between the observation direction and the velocity vector of the moving source lies in . If otherwise, one can only expect to recover a strip thinner than this smallest strip for straight and circular motions. The far-field data measured at sparse observable directions can be used to recover the -convex domain of the trajectory. Both two- and three-dimensional numerical examples are implemented to show effectiveness and feasibility of the approach.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136272285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Singular Value Decomposition of the Wave Forward Operator with Radial Variable Coefficients 径向变系数波正演算子的奇异值分解

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-11 DOI: 10.1137/22m1511643

Minam Moon, Injo Hur, Sunghwan Moon

引用次数: 0

Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown View Tomography 基于空间一致性的三维未知视图层析成像正交矩阵检索

3区数学

SIAM Journal on Imaging Sciences Pub Date : 2023-08-08 DOI: 10.1137/22m1498218

Shuai Huang, Mona Zehni, Ivan Dokmanić, Zhizhen Zhao

{"title":"Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown View Tomography","authors":"Shuai Huang, Mona Zehni, Ivan Dokmanić, Zhizhen Zhao","doi":"10.1137/22m1498218","DOIUrl":"https://doi.org/10.1137/22m1498218","url":null,"abstract":"Unknown view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. A line of work starting with Kam (1980) employs the method of moments with rotation-invariant Fourier features to solve UVT in the frequency domain, assuming that the orientations are uniformly distributed. This line of work includes the recent orthogonal matrix retrieval (OMR) approaches based on matrix factorization, which, while elegant, either require side information about the density that is not available or fail to be sufficiently robust. For OMR to break free from those restrictions, we propose to jointly recover the density map and the orthogonal matrices by requiring that they be mutually consistent. We regularize the resulting nonconvex optimization problem by a denoised reference projection and a nonnegativity constraint. This is enabled by the new closed-form expressions for spatial autocorrelation features. Further, we design an easy-to-compute initial density map which effectively mitigates the nonconvexity of the reconstruction problem. Experimental results show that the proposed OMR with spatial consensus is more robust and performs significantly better than the previous state-of-the-art OMR approach in the typical low signal-to-noise-ratio scenario of 3D UVT.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135746488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1