改进䲟鱼优化算法和熵测度的图像多阈值分割OA北大核心CSTPCD

To improve the poor segmentation quality of traditional image thresholding segmentation techniques,this study proposes an image multilevel thresholding segmentation method.This method is based on an improved remora op-timization algorithm and entropy measure,specifically called the weight lens remora optimization algorithm(WLROA).First,lens opposition-based learning was used to generate the lens opposite solution.This approach bolstered population diversity and improved the algorithm's ability to overcome local optimal solutions.Furthermore,an adaptive weight factor was introduced to perturb the individuals'positions appropriately.This modification aimed to improve the al-gorithm's exploratory ability.The optimization objective was to minimize cross entropy.To achieve this,WLROA was used to determine the minimum cross entropy and obtain the corresponding thresholds.A selection of images from the Berkeley segmentation data set and remote sensing images were selected to assess the segmentation performance of the proposed algorithm.These results were then compared with those from other methods.The results revealed that,in com-parison with other well-known algorithms,WLROA yielded better segmentation results and proved effective in accur-ately processing complex images.