Titlebook: Handbook of Mathematical Methods in Imaging; Otmar Scherzer Living reference work 20200th edition

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書目名稱Handbook of Mathematical Methods in Imaging影響因子(影響力)




書目名稱Handbook of Mathematical Methods in Imaging影響因子(影響力)學科排名




書目名稱Handbook of Mathematical Methods in Imaging網絡公開度




書目名稱Handbook of Mathematical Methods in Imaging網絡公開度學科排名




書目名稱Handbook of Mathematical Methods in Imaging被引頻次




書目名稱Handbook of Mathematical Methods in Imaging被引頻次學科排名




書目名稱Handbook of Mathematical Methods in Imaging年度引用




書目名稱Handbook of Mathematical Methods in Imaging年度引用學科排名




書目名稱Handbook of Mathematical Methods in Imaging讀者反饋




書目名稱Handbook of Mathematical Methods in Imaging讀者反饋學科排名

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Energy Minimization Methods,etween energy minimization and modeling via the choice of the energy is thus established. Examples and illustrations corroborate the presented results. Applications that take benefit from these results are presented as well.

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Large-Scale Inverse Problems in Imaging,hat can exploit structure in the problem. Examples from image deconvolution, multi-frame blind deconvolution, and tomosynthesis illustrate the potential of these algorithms. Much progress has been made in the field of large-scale inverse problems, but many challenges still remain for future research.

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Microlocal Analysis in Tomography,l properties of the tomographic transforms are given and then used to explain the observed strengths and limitations of the reconstructions. This will show that these limitations are intrinsic to these limited data problems themselves.

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Optical Flow, research directions from a broader perspective. The state of the art and corresponding deficiencies are reported along with directions of future research. The presentation aims at providing an accessible guide for practitioners as well as stimulating research work in relevant fields of mathematics and computer vision.

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Living reference work 20200th editionctronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. .It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful..

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Differential Methods for Multidimensional Visual Data Analysis,examples from current research on the application of these mathematical domains to scientific visualization. Ultimately the use of these methods allows for a systematic approach for image generation resulting from the analysis of multidimensional datasets.

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RLS898

Mathematical Methods in PET and SPECT Imaging,for PET. These studies, which use both simulated and real data and which employ a variety of image quality measures including contrast and bias, indicate that SRT has certain advantages in comparison with FBP.