已经证明2维情况下一般各向异性扩散与HAAR小波收缩在一定条件下是等价的，基于此等价性的各向异性小波收缩结合了小波收缩与各向异性扩散两种方法的优势。将各向异性小波收缩用于多尺度图像分割，提出一种对多尺度各向异性扩散分割方法的改进方法——多尺度各向异性小波收缩图像分割算法。该算法利用各向异性小波收缩对图像中像素灰度值进行扩散，在尽可能保持边缘的情况下，使同质区域内相邻像素灰度随尺度数增加趋于相同，构造基于尺度的空间栈，从而完成对目标的分割，是一种非监督图像分割方法。对比实验结果表明，该算法在有效处理区域内部不一致性的同时，能够准确地定位目标边缘，实现同质区域的融合，完成分割任务，且该算法收敛速度高于多尺度各向异性扩散分割方法。

The equivalence of the anisotropic diffusion and HAAR wavelet shrinkage in 2-dimension in a given condition has been proved. The anisotropic wavelet shrinkage based on the equivalence combines the merits of wavelet shrinkage and anisotropic diffusion. In this paper, applying the anisotropic wavelet shrinkage to multiresolution image segmentation is researched, one improved method to multi-resolution anisotropic diffusion image segmentation method is proposed, which is a multi-resolution anisotropic wavelet shrinkage image segmentation method. The anisotropic wavelet shrinkage is used to diffuse the pixels in the image, and the gray level of the neighbor pixels in homogenous fields tend to become the same value along with the scale’s increase while keeping the edge as far as possible. The scale space stack based on scale is built, and the segmentation to object is accomplished. It is an unsupervised method. Comparison experiments show that the method can locate the edge of the object exactly while processing the internal inconsistency of fields effectively, and the homogenous fields are merged perfectly to implement segmentation. The convergence speed of the method is faster than the multi-resolution anisotropic diffusion image segmentation method.