This thesis analyzes the theoretics of a space-adaptive regularization method for blind image restoration.
对空间自适应正则化图像盲复原方法的理论和相应算法进行分析。
Secondly, to perfect the known restoring models, a new space-adaptive regularization model of image restoration is constructed by redesigning regularized parameter and regularized item.
第二，在现有复原模型的完善上，重新构建正则化参数与正则化项，构造了新的具有空间自适应性质的正则化图像复原模型。
A set of local interaction field and adaptive regularizers are introduced to usual regularization theory.
在标准正则化理论中建议一类局部相互作用场和自适应正则项。
An adaptive filter for video image sequence super resolution reconstruction is proposed on the basis of the weighted least square and PDE regularization in this paper.
在加权最小二乘的基础上，结合PDE正则化，提出了一种视频图像序列超分辨率重建的自适应滤波方法。
The regularization matrix will be chosen by prior information of the model parameters and the adaptive factor, however, will be determined by posterior information.
其基本思想是利用验前模型信息确定正则化矩阵，利用验后观测信息确定自适应因子。
The regularization matrix will be chosen by prior information of the model parameters and the adaptive factor, however, will be determined by posterior information.
其基本思想是利用验前模型信息确定正则化矩阵，利用验后观测信息确定自适应因子。
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