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受云液水含量、 正则算子特性及边界因素的综合影响， 不同云型的反演精度存在差异。
Besides, the retrieval accuracy also varies in different cloud types because of the magnitude of the LWC, the characteristic of the regular operator, and the lateral boundary factor.
该方法可利用均值-方差间关系等先验知识来构造加权矩阵，并利用二维局部空间信息来构造惩罚项或正则算子。
It utilizes the prior variance -mean relationship to construct the weight matrix and the two -dimensional (2d) spatial information as the penalty or regularization operator.
在本文条件下，我们论证正则化算子取拉普拉斯算子比取恒等算子恢复性能好，并且预测噪声能量。
And we demonstrate that Lapalacian operator is better than identity operator in the condition of this paper, predict the noise energy.
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