A Multi-Strategy Improved Dung Beetle Optimizer for the Kapur Entropy Multi-Threshold Image Segmentation Algorithm

To address the issues of detail loss and unstable segmentation quality in image segmentation, this paper proposes a multi-strategy improved dung beetle optimization algorithm be applied to multi-threshold image segmentation. Thus, we have developed a multi-strategy improved dung beetle optimizer Kapur entropy multi-threshold image segmentation algorithm (MIDBO-KMIA). This algorithm enhanced global search capability and convergence stability, improved segmentation accuracy and algorithm robustness, and solved the problems of detail preservation and segmentation quality in complex scenarios. Firstly, Sobol sequences were adopted to initialize the population, enhancing its diversity. Secondly, a multi-stage perturbation update mechanism was introduced to prevent convergence to local optima and improved global exploration. Thirdly, the convergence precision was further improved by optimizing the hybrid dynamic switching mechanism and proposing dynamic mutation update and distance selection update strategies. Finally, the MIDBO algorithm was applied to Kapur entropy multi-threshold image segmentation, and experimental research was conducted using Peak Signal-to-Noise Ratio (PSNR), SIMilarity index (SSIM), and Feature SIMilarity index (FSIM) as evaluation metrics. The experimental results demonstrate that the performance of the multi-strategy improved dung beetle optimization Kapur entropy multi-threshold image segmentation algorithm is significantly better than that of other algorithms, and that it can more effectively solve the problems of detail preservation and segmentation quality in complex scenes, and enhance the ability to adapt to complex image scenes.