论文标题
使用回归分解和合成的空间分辨率增强了超采样图像
Spatial Resolution Enhancement of Oversampled Images Using Regression Decomposition and Synthesis
论文作者
论文摘要
提出了一种针对带有稀疏设计矩阵的回归分析的新统计模型。该新模型利用了设计矩阵中有限的非零元素的位置将回归模型分解为子回归模型。进一步对这些有限的非零元素的值进行了统计推断，以提供综合这些子回归模型的参考。有了回归分解和合成的概念，可以将有关设计矩阵结构的信息纳入回归分析中，以提供更可靠的估计。然后，将提出的模型应用于空间过采样图像的空间分辨率增强问题。为了系统地评估所提出的模型在增强空间分辨率方面的性能，将提出的方法应用于通过随机场模拟复制的超采样图像。这些应用程序基于不同生成的方案的结果，结论了所提出方法在增强空间分辨率分辨出空间过采样图像方面的有效性和可行性。
A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.
