与传统的阈值层叠滤波器相比，镜像阈值层叠滤波器不仅具有低通滤波的特性，还具有带通和高通的特性。但由于镜像阈值层叠滤波器比传统的阈值层叠滤波器的正布尔函数长度有显著增加，从而使计算量增加，为解决这一问题，提出了一种镜像自适应加权（MAW）算法。该方法充分考虑了镜像阈值分解的特点，并通过引入自适应领域加权误差准则建立了代价向量，在迭代过程中，对代价向量的层叠性进行快速约束，并判断其收敛性，最终获得了基于最优正布尔函数的自适应加权镜像阈值层叠滤波器（AWMSF）。为了验证该滤波器的滤噪性能，对最优AWMSF进行了性能分析，结果表明，AWMSF在滤除噪声的同时，能更好地保持图像的细节信息，并可减少迭代次数，从而使计算复杂度大大降低。

Compared to the traditional threshold stack filters, mirrored threshold stack filters have been empowered not only with lowpass filtering characteristics but with bandpass and highpass characteristics as well, but their positive Boolean functions length leads to an increasing restriction during calculation. In order to solve the above problems, this paper proposes a mirrored adaptive weight (MAW) algorithm, which calculates cost vector based on adaptive neighbor weight error criterion (ANWMAE). After cost vector stacking is restricted and its astringency is estimated, the optimal positive Boolean function of stack filters is confirmed to construct adaptive weight mirrored threshold stack filters (AWMSF). In order to testify the filtering capability, AWMSF was simulated, the results show that it can suppress noise and protect the details of image effectively, the number of iterations is reduced and the computing complication is decreased rapidly too.