Inverse Problems最新文献

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Geometric approach for determining stationary phase points in radar imaging 确定雷达成像中静止相位点的几何方法
Inverse Problems Pub Date : 2024-02-02 DOI: 10.1088/1361-6420/ad2530
Yixiang Luomei, Tiantian Yin, Kai Tan, Xudong Chen
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引用次数: 0
Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms 通过收敛基元-二元算法去除非凸加权变分金属伪影
Inverse Problems Pub Date : 2024-02-02 DOI: 10.1088/1361-6420/ad2694
Lianfang Wang, Zhangling Chen, Zhifang Liu, Yutong Li, Yunsong Zhao, Hongwei Li, Huibin Chang
{"title":"Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms","authors":"Lianfang Wang, Zhangling Chen, Zhifang Liu, Yutong Li, Yunsong Zhao, Hongwei Li, Huibin Chang","doi":"10.1088/1361-6420/ad2694","DOIUrl":"https://doi.org/10.1088/1361-6420/ad2694","url":null,"abstract":"\u0000 Direct reconstruction through filtered back projection engenders metal artifacts in polychromatic computed tomography images, attributed to highly attenuating implants, which further poses great challenges for subsequent image analysis. Inpainting the metal trace directly in the Radon domain for the extant variational method leads to strong edge diffusion and potential inherent artifacts. With normalization based on pre-segmentation, the inpainted outcome can be notably ameliorated. However, its reconstructive fidelity is heavily contingent on the precision of the pre-segmentation, and highly accurate segmentation of images with metal artifacts is non-trivial in actuality. In this paper, we propose a nonconvex weighted variational approach for metal artifact reduction. Specifically, in lieu of employing a binary function with zeros in the metal trace, an adaptive weight function is designed in the Radon domain, with zeros in the overlapping regions of multiple disjoint metals as well as areas of highly attenuated projections, and the inverse square root of the measured projection in other regions. A nonconvex $L^1-alpha L^2$ regularization term is incorporated to further enhance edge contrast, alongside a box-constraint in the image domain. Efficient first-order primal-dual algorithms, proven to be globally convergent and of low computational cost owing to the closed-form solution of all subproblems, are devised to resolve such a constrained nonconvex model. Both simulated and real experiments are conducted with comparisons to other variational algorithms, validating the superiority of the presented method. Especially in comparison to the reweighted JSR, our proposed algorithm can curtail the total computational cost to at most one-third, and for the case of inaccurate pre-segmentation, the recovery outcomes by the proposed algorithms are notably enhanced.","PeriodicalId":508687,"journal":{"name":"Inverse Problems","volume":"14 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870769","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
Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms 通过收敛基元-二元算法去除非凸加权变分金属伪影
Inverse Problems Pub Date : 2024-02-02 DOI: 10.1088/1361-6420/ad2694
Lianfang Wang, Zhangling Chen, Zhifang Liu, Yutong Li, Yunsong Zhao, Hongwei Li, Huibin Chang
{"title":"Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms","authors":"Lianfang Wang, Zhangling Chen, Zhifang Liu, Yutong Li, Yunsong Zhao, Hongwei Li, Huibin Chang","doi":"10.1088/1361-6420/ad2694","DOIUrl":"https://doi.org/10.1088/1361-6420/ad2694","url":null,"abstract":"\u0000 Direct reconstruction through filtered back projection engenders metal artifacts in polychromatic computed tomography images, attributed to highly attenuating implants, which further poses great challenges for subsequent image analysis. Inpainting the metal trace directly in the Radon domain for the extant variational method leads to strong edge diffusion and potential inherent artifacts. With normalization based on pre-segmentation, the inpainted outcome can be notably ameliorated. However, its reconstructive fidelity is heavily contingent on the precision of the pre-segmentation, and highly accurate segmentation of images with metal artifacts is non-trivial in actuality. In this paper, we propose a nonconvex weighted variational approach for metal artifact reduction. Specifically, in lieu of employing a binary function with zeros in the metal trace, an adaptive weight function is designed in the Radon domain, with zeros in the overlapping regions of multiple disjoint metals as well as areas of highly attenuated projections, and the inverse square root of the measured projection in other regions. A nonconvex $L^1-alpha L^2$ regularization term is incorporated to further enhance edge contrast, alongside a box-constraint in the image domain. Efficient first-order primal-dual algorithms, proven to be globally convergent and of low computational cost owing to the closed-form solution of all subproblems, are devised to resolve such a constrained nonconvex model. Both simulated and real experiments are conducted with comparisons to other variational algorithms, validating the superiority of the presented method. Especially in comparison to the reweighted JSR, our proposed algorithm can curtail the total computational cost to at most one-third, and for the case of inaccurate pre-segmentation, the recovery outcomes by the proposed algorithms are notably enhanced.","PeriodicalId":508687,"journal":{"name":"Inverse Problems","volume":"20 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139811002","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
Geometric approach for determining stationary phase points in radar imaging 确定雷达成像中静止相位点的几何方法
Inverse Problems Pub Date : 2024-02-02 DOI: 10.1088/1361-6420/ad2530
Yixiang Luomei, Tiantian Yin, Kai Tan, Xudong Chen
{"title":"Geometric approach for determining stationary phase points in radar imaging","authors":"Yixiang Luomei, Tiantian Yin, Kai Tan, Xudong Chen","doi":"10.1088/1361-6420/ad2530","DOIUrl":"https://doi.org/10.1088/1361-6420/ad2530","url":null,"abstract":"\u0000 This paper proposes a geometric approach to find out the stationary phase points in radar imaging, namely the coordinates of the transmitting and the receiving antennas that have the greatest contribution to a specified point in the wavenumber spectrum of the scattered field. The foundation of the proposed approach is that, the Green’s function can be approximated by a locally plane wave when the distance between the individual antennas and the object is much larger than the wavelength. In this way, the stationary phase points can be easily found through geometric calculations, much more convenient than the prevailing algebraic approach of letting the first derivative of the phase term to be zero. Six examples for different propagation backgrounds and antenna setups are provided in this paper. It is demonstrated that, the proposed method is beneficial in eliminating extraneous roots of the stationary phase points, which could be generated by letting the first derivative of the phase term to be zero in the stationary phase method.","PeriodicalId":508687,"journal":{"name":"Inverse Problems","volume":"50 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139870602","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
A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets 用于片状均质目标微波成像的深度学习增强型反向散射框架
Inverse Problems Pub Date : 2024-02-02 DOI: 10.1088/1361-6420/ad2532
Álvaro Yago Ruiz, Marija Nikolic Stevanovic, Marta Cavagnaro, Lorenzo Crocco
{"title":"A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets","authors":"Álvaro Yago Ruiz, Marija Nikolic Stevanovic, Marta Cavagnaro, Lorenzo Crocco","doi":"10.1088/1361-6420/ad2532","DOIUrl":"https://doi.org/10.1088/1361-6420/ad2532","url":null,"abstract":"\u0000 In this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, rawdata are processed via Orthogonality Sampling Method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation.","PeriodicalId":508687,"journal":{"name":"Inverse Problems","volume":"60 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139683724","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
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