Two-dimensional Otsu Multi-Threshold Image Segmentation Based on Hybrid Whale Optimization Algorithm

Threshold segmentation is a commonly used method to deal with image segmentation problems. Aiming at the problems of the traditional maximum inter-class variance method (Otsu) in multi-threshold image segmentation, such as large amount of computation, long computation time and low segmentation accuracy. This paper proposes a two-dimensional Otsu multi-threshold image segmentation algorithm based on hybrid whale optimization algorithm. Firstly, the two-dimensional Otsu single-threshold segmentation method is extended to the two-dimensional Otsu multi-threshold segmentation method to improve the segmentation effect. At the same time, in order to reduce the calculation time and improve the solution accuracy, the new hybrid whale optimization algorithm proposed in this paper is used to calculate the threshold. The test is carried out through a set of classical image threshold segmentation sets, and the widely used image segmentation evaluation standards PSNR and SSIM are used for judgment. The results of this paper are also compared with the results of other novel algorithms, including the results of one-dimensional Otsu multi-threshold segmentation method. The results show that the proposed two-dimensional Otsu single-threshold segmentation improves the segmentation efficiency and quality, it is an effective image segmentation method.