38
个编辑
更改
跳到导航
跳到搜索
第147行：
第147行： − + 第191行：
第191行： − + 第218行：
第218行： − Math ( lambda { h }) ^ k ( lambda { h }) ^ { k-1} + gamma { h }(h ^ k- nabla (d { h }) ^ k) / math 第224行：
第223行： − Math ( lambda { v }) ^ k ( lambda { v }) ^ { k-1} + gamma { v }(v ^ k- nabla (d { v }) ^ k) / math
无编辑摘要
=====Edge-preserving total variation (TV) based compressed sensing<ref name ="EPTV">{{cite journal | last1 = Tian | first1 = Z. | last2 = Jia | first2 = X. | last3 = Yuan | first3 = K. | last4 = Pan | first4 = T. | last5 = Jiang | first5 = S. B. | year = 2011 | title = Low-dose CT reconstruction via edge preserving total variation regularization | url = | journal = Phys Med Biol | volume = 56 | issue = 18| pages = 5949–5967 | doi=10.1088/0031-9155/56/18/011| pmid = 21860076 | pmc = 4026331 | arxiv = 1009.2288 | bibcode = 2011PMB....56.5949T }}</ref>=====
=====Edge-preserving total variation (TV) based compressed sensing<ref name ="EPTV">{{cite journal | last1 = Tian | first1 = Z. | last2 = Jia | first2 = X. | last3 = Yuan | first3 = K. | last4 = Pan | first4 = T. | last5 = Jiang | first5 = S. B. | year = 2011 | title = Low-dose CT reconstruction via edge preserving total variation regularization | url = | journal = Phys Med Biol | volume = 56 | issue = 18| pages = 5949–5967 | doi=10.1088/0031-9155/56/18/011| pmid = 21860076 | pmc = 4026331 | arxiv = 1009.2288 | bibcode = 2011PMB....56.5949T }}</ref>=====
基于边缘保留的总变分（TV）的压缩感知<ref name="EPTV" />
基于边缘保留的总变分（TV）的压缩感知<ref name="EPTV" />
[[文件:Edge preserving TV.png|链接=link=link=link=Special:FilePath/Edge_preserving_TV.png|替代=|缩略图|Flow diagram figure for edge preserving total variation method for compressed sensing]]
[[文件:Edge preserving TV.png|链接=link=link=link=Special:FilePath/Edge_preserving_TV.png|缩略图|Flow diagram figure for edge preserving total variation method for compressed sensing]]
Flow diagram figure for edge preserving total variation method for compressed sensing
Flow diagram figure for edge preserving total variation method for compressed sensing
[[文件:Augmented Lagrangian.png|链接=link=link=link=Special:FilePath/Augmented_Lagrangian.png|替代=|缩略图|Augmented Lagrangian method for orientation field and iterative directional field refinement models]]
[[文件:Augmented Lagrangian.png|链接=link=link=link=Special:FilePath/Augmented_Lagrangian.png|缩略图|Augmented Lagrangian method for orientation field and iterative directional field refinement models]]
Augmented Lagrangian method for orientation field and iterative directional field refinement models
Augmented Lagrangian method for orientation field and iterative directional field refinement models
<math>(\lambda_{H})^k = (\lambda_{H})^{k-1} + \gamma_{H}(H^k - \nabla (d_{h})^k)</math>
<math>(\lambda_{H})^k = (\lambda_{H})^{k-1} + \gamma_{H}(H^k - \nabla (d_{h})^k)</math>
<math>(\lambda_{V})^k = (\lambda_{V})^{k-1} + \gamma_{V}(V^k - \nabla (d_{v})^k)</math>
<math>(\lambda_{V})^k = (\lambda_{V})^{k-1} + \gamma_{V}(V^k - \nabla (d_{v})^k)</math>