To consider the differences between infrared image and common gray level image

the effects of regional division are not good when the commonly used gray level-average gray level 2D histogram is used in infrared image segmentation. An improved Otsu threshold selection method based on gray level-gradient 2D histogram is proposed in this paper. The appropriate gradient operator is chosen. An improved particle swarm optimization(PSO) algorithm is used to search for the segmentation threshold. An effective method that identifies premature stagnation is embedded to PSO

so once premature stagnation happens

a randomized solution

as a substitute for current optimum

is used for particles to go out of the local optima. The simulation experiments demonstrate that the algorithm proposed in this paper achieves accurate borders and clear details of features after infrared image threshold because of the new 2D histogram. The compute speed is improved effectively.