Fast infrared image segmentation method based on 2D OTSU and particle swarm optimization

摘要

The image segmentation method based on ID histogram and the optimal objective function is an important threshold segmentation method, but if it is applied to the infrared image segmentation directly, its ability for the suppression of the background noise is weak. In this paper, the 2D Maximum inter-class variance method is applied to infrared image segmentation, which improves the image segmentation effect obviously, but it takes a long time to calculate. Therefore, an improved Particle Swarm Optimization (PSO) algorithm is introduced to speed up the algorithm, which improves the real-time performance of the algorithm. The experimental results show that the new method has not only good segmentation effect, but also high computational efficiency, and it is a fast infrared image segmentation method.