复小波变换虽然具有良好的方向选择性和平移不变性，但不具备完全重构性条件，而二元树复小波变换(DTCWT)正好解决了这一难题.在分析二元树复小波分解后的 12个高频子带方向性的基础上，利用其良好的方向选择性提出了一种对线形纹理图象进行增强滤波的方法.该方法借助于小波变换域的方向解析性，在各子带中保留图象中各局部主方向的信息而滤除其他方向的噪声.利用该方法进行滤波还可以避免对信号和噪声频率特性和统计特性进行估计，从而大大减小了滤波的复杂程度.以指纹图象为例的实验结果表明，该方法效果较好，便于实现，尤其适用于噪声特性复杂的纹理图象的滤波.

Complex wavelets can provide both shift invariance and good directional selectivity, which are lack in the traditional wavelet transform, but can not satisfy the condition of perfect reconstruction. Dual tree complex wavelet transform (DTCWT), which employs a dual tree of wavelet filters to obtain the real and imaginary parts of complex wavelet coefficients, can solve this problem. In this paper, the principle of DTCWT is discussed, and the directional characteristics of the twelve high frequency sub bands after DTCWT are studied. Based on the good directional characteristics of DTCWT, we propose a directional filtering method for enhancement of curve like texture images. Image filtering by using this method, the information of the local main direction in each sub band of wavelet transform domain is reserved, and the noise distributed in other directions is removed. This method is proven to be not only less complex, since it avoids the frequency and statistical estimations on characteristics of both the signal and noise, but also on better directional selectivity than real wavelet transform. The experimental results on texture image enhancement demonstrate that this method is more efficient and also more suitable for complicated textures images.