公开(公告)号：US20130286041A1

公开(公告)日：2013-10-31

申请号：US13564195

申请日：2012-08-01

申请人： Stephen KamLing CHIU ,  Phil Dean ANNO

发明人： Stephen KamLing CHIU ,  Phil Dean ANNO

IPC分类号： G09G5/00

CPC分类号： G01V1/28 ,  G01V1/36 ,  G01V2210/57

摘要： A process for overcoming aliasing using a minimum weighted norm interpolation (MWNI) technique may include computing an initial, regularly interpolated model with no data gaps and computing a plurality of initial spectral weights using the initial, regularly interpolated model. The initial, regularly interpolated model is used to compute the spectral weights as initial constraints in a least-squares solution methodology. The initial spectral weights are used as initial constraints in a constrained minimum weighted norm interpolation data reconstruction. The process may further include converting the initial, regularly interpolated model into a frequency domain and computing unknown spectral weights from frequency data at each frequency slice of the initial, regularly interpolated model using Fourier transform. The process results in reducing aliasing artifacts and improving data regularization.

摘要翻译： 使用最小加权范数内插（MWNI）技术来克服混叠的过程可以包括使用初始的经常内插的模型来计算没有数据间隙的初始的，经常内插的模型并且计算多个初始频谱权重。 初始的，经常插值的模型用于计算最小二乘解决方法中的初始约束的光谱权重。 初始频谱权重用作约束最小加权范数内插数据重建中的初始约束。 该过程还可以包括将初始的经常内插的模型转换成频域，并且使用傅里叶变换在初始的，有规则的内插模型的每个频率片段的频率数据中计算未知频谱权重。 该过程导致减少混叠伪像并改善数据正则化。