现有的 Arimoto 熵阈值法仅依赖于灰度直方图分布，且计算最佳阈值时需搜索整个解空间，效率不高． 为此，文中提出了一种二维 Arimoto 灰度熵阈值分割的快速迭代算法． 首先，提出了一维 Arimoto 灰度熵阈值选取的快速迭代算法;然后，考虑图像目标和背景的类内灰度均匀性，导出了基于灰度 － 平均灰度级直方图的 Arimoto 灰度熵阈值法，并给出了中间变量的快速递推公式;最后，提出了二维 Arimoto 灰度熵阈值选取的快速迭代算法，推导了相应的公式，大大减少了运算量． 实验结果表明，文中所提算法运行速度快，分割性能优于现有的 5 种同类阈值分割算法，分割后图像中的目标完整，边缘纹理清晰，细节更为丰富．

As the existing Arimoto entropy-based thresholding methods only depend on the probability information from gray histogram and need to search the whole solution space to obtain the optimal threshold with low efficiency,a fast iterative algorithm for threshold segmentation on the basis of two-dimensional Arimoto gray entropy is pro- posed.Firstly,a fast iterative algorithm for threshold selection using one-dimensional Arimoto gray entropy is pro- posed.Secondly,by taking into consideration the gray level uniformity within the object cluster and the back- ground cluster,a two-dimensional Arimoto gray entropy thresholding method on the basis of gray level-average gray level histogram is derived.Then,fast recursive formulae for intermediate variables are given.Finally,a fast ite- rative algorithm is proposed for threshold selection on the basis of two-dimensional Arimoto gray entropy,and the corresponding algorithmic formulae are derived,which helps reduce computation burden greatly.Experimental results show that the proposed algorithm is superior to five existing threshold segmentation algorithms because it runs more rapidly and is more effective in obtaining segmented images with complete objects,clear edges and rich details.