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    Using an MM-Principle to Achieve Fast Image Data Estimation from Large Image Data Sets
    2.
    发明申请
    Using an MM-Principle to Achieve Fast Image Data Estimation from Large Image Data Sets 有权
    使用MM原理从大图像数据集中实现快速图像数据估计

    公开(公告)号：US20150279082A1

    公开(公告)日：2015-10-01

    申请号：US14184446

    申请日：2014-02-19

    Applicant: Howard University

    Inventor： John M. M. Anderson ,  Mandoye Ndoye

    IPC: G06T15/08 ,  G01S7/04 ,  G01S13/88

    CPC classification number: G06T15/08 ,  G01S7/046 ,  G01S7/295 ,  G01S13/003 ,  G01S13/885 ,  G01S13/90

    Abstract: A majorize-minimize (MM) mathematical principle is applied to least squares regularization estimation problems to effect efficient processing of image data sets to provide good quality images. In a ground penetrating radar application, these approaches can reduce processing time and memory use by accounting for a symmetric nature of a given radar pulse, accounting for similar discrete time delays between transmission of a given radar pulse and reception of reflections from the given radar pulse, and accounting for a short duration of the given radar pulse.

    Abstract translation: 主要最小化（MM）数学原理被应用于最小二乘法正则化估计问题，以实现图像数据集的有效处理，以提供高质量的图像。 在地面穿透雷达应用中，这些方法可以通过考虑给定雷达脉冲的对称性来减少处理时间和存储器使用，考虑给定雷达脉冲的传输和从给定的雷达脉冲接收反射之间的类似的离散时间延迟 ，并且考虑给定雷达脉冲的短时间。

    Using An MM-Principle to Enforce a Sparsity Constraint on Fast Image Data Estimation From Large Image Data Sets
    4.
    发明申请
    Using An MM-Principle to Enforce a Sparsity Constraint on Fast Image Data Estimation From Large Image Data Sets 有权
    使用MM原理来强制从大图像数据集快速图像数据估计的稀疏约束

    公开(公告)号：US20160202346A1

    公开(公告)日：2016-07-14

    申请号：US14305934

    申请日：2014-06-16

    Applicant: Howard University

    Inventor： John M. M. Anderson ,  Mandoye Ndoye ,  Oludotun Ode ,  Henry C. Ogworonjo

    IPC: G01S7/292 ,  G01S13/88

    CPC classification number: G01S7/292 ,  G01S7/2923 ,  G01S13/0209 ,  G01S13/885 ,  G01S13/887 ,  G01S13/89 ,  G06K9/00791 ,  G06K9/6277

    Abstract: The mathematical majorize-minimize principle is applied in various ways to process the image data to provide a more reliable image from the backscatter data using a reduced amount of memory and processing resources. A processing device processes the data set by creating an estimated image value for each voxel in the image by iteratively deriving the estimated image value through application of a majorize-minimize principle to solve a maximum a posteriori (MAP) estimation problem associated with a mathematical model of image data from the data. A prior probability density function for the unknown reflection coefficients is used to apply an assumption that a majority of the reflection coefficients are small. The described prior probability density functions promote sparse solutions automatically estimated from the observed data.

    Abstract translation: 数学主要最小化原理以各种方式应用于处理图像数据，以使用减少量的存储器和处理资源从后向散射数据提供更可靠的图像。 处理装置通过使用主要最小化原理迭代地导出估计图像值来求解与数学模型相关联的最大后验（MAP）估计问题，通过为图像中的每个体素创建估计图像值来处理数据集 的图像数据。 用于未知反射系数的先验概率密度函数用于应用大多数反射系数小的假设。 所描述的先验概率密度函数促进从观测数据自动估计的稀疏解。

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