A robust fusion algorithm based on noise variance estimation is presented.
给出一种基于噪声方差估计的稳健融合算法。
Instead of many algorithms rely on accurate estimation of noise variance, this algorithm estimates noise energy through DCT which extracts local feature.
该算法不依赖于对噪声方差进行估计，直接利用DCT变换对高频各个子带进行局部特征提取，从而估计噪声能量的估计阈值。
When the noise variance is known, and it's process is stationary, the signal can be enhanced by use of the recursive least mean-square estimation.
当噪声的方差已知，且过程是平稳的，应用递推最小方差估计，能够增强信号。
FATAFE realizes unbiased estimation and its estimated variance approaches to theoretical lower bound if only signal-to-noise ratio is not very low.
只要信噪比不甚低，频点自跟踪频率估计是无偏的且估计方差接近理论下限。
Variance estimation is a common problem in noised image processing. The basic idea is to get a sub-image that includes only "pure" noise to estimate the variance of original noise.
噪声的方差估计是含噪图像处理中的常见问题之一，其基本思想是通过某种方法寻找含噪图像中的“纯”噪声子图像来估计原噪声方差。
Variance estimation is a common problem in noised image processing. The basic idea is to get a sub-image that includes only "pure" noise to estimate the variance of original noise.
噪声的方差估计是含噪图像处理中的常见问题之一，其基本思想是通过某种方法寻找含噪图像中的“纯”噪声子图像来估计原噪声方差。
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