An Improved Northern Goshawk Optimization Algorithm for Mural Image Segmentation.

In the process of mural protection and restoration, using optimization algorithms for image segmentation is a common method for restoring mural details. However, existing optimization-based image segmentation methods often lack image segmentation quality. To alleviate the aforementioned issues, this paper proposes a mural image segmentation algorithm based on OPBNGO by integrating the Northern Goshawk Optimization (NGO) algorithm with the off-center learning strategy, partitioned learning strategy, and Bernstein-weighted learning strategy. In OPBNGO, firstly, the off-center learning strategy is proposed, which effectively improves the global search ability of the algorithm by utilizing biased center individuals. Secondly, the partitioned learning strategy is introduced, which achieves a better balance between the exploration and development phases by applying diverse learning methods to the population. Finally, the Bernstein-weighted learning strategy is proposed, which effectively improves the algorithm's development performance. Subsequently, the OPBNGO algorithm is applied to solve the image segmentation problem for eight mural images. Experimental results show that it achieves a winning rate of over 96.87% in terms of fitness function value, achieves a winning rate of over 93.75% in terms of FSIM, SSIM, and PSNR metrics, and can be considered a promising mural image segmentation algorithm.