中文摘要：

在图像多尺度分析时, 为了对后续的图像处理提供高质量的特征输入, 在一维局部均值分解算法基础上提出一种二维局部均值分解算法. 首先采用优化的8一邻域算子求取图像中的局部极值点; 然后针对鞍点对求解局部相邻极值点时的影响, 提出一种基于自适应窗口的搜寻方法, 以控制局部相邻极值点数求取局部相邻极值点, 进而得到平滑的包络估计函数和局部均值函数; 最后依据包络估计函数和局部均值函数, 通过迭代寻优得到相应的乘积函数将图像分解成不同尺度下的成分. 在人工合成图像与典型图像的多尺度分析处理结果表明, 该算法可行有效; 与二维经验模态分解算法的比较结果表明, 该算法具有更快的速度和更好的处理效果; 并对该算法中的重要参数进行了敏感性分析, 验证了算法具有较好的鲁棒性, 给出了比较合理的参数取值范围.

英文摘要：

Multiscale image analysis provides important feature inputs for the further image processing. This paper proposes a new multiscale image analysis method called bidimensional local mean decomposition (BLMD) on the basis of local mean decomposition(LMD). Firstly, BLMD uses 8-neighborhood operator to obtain local extreme points; In order to eliminate the influence of saddle points when searching the local ad-jacent extreme, this paper proposes an adaptive window-based search method to control the number of local adjacent extreme points; Finally BLMD calculates the smooth envelope estimation function and local mean function to generate the product function, which decomposes images into different scale components. The results on synthetic images and typical real-world images indicate that BLMD is effective for multi-scale image analysis. In comparison with bidimensional empirical mode decomposition (BEMD), BLMD presents the more effective and fast image processing results. In addition, the parameter sensitivity analysis approves that BLMD shows robust performance in image processing. Finally, the reasonable ranges of some key pa-rameters for BLMD are given in this paper.