中文摘要：

针对红外图像强背景和低反差的特点，提出了一种基于粒子群优化的激光光斑图像增强算法。结合概率统计的思想，对激光光斑图像进行“三阶矩不变”图像分割，提取出了需要增强的目标，在“γ-修正”算法的基础上演化建立了4种数学模型，并对这4种数学模型分别进行求解，提出了一种基于粒子群优化的改进“（n、b、γ）-修正”算法，将该算法应用于激光光斑目标的增强过程中。实验结果表明，远红外激光光斑的平均强度和信息熵分别提高了5．395和1．326倍，近红外激光光斑的平均强度和信息熵分别提高了2．458和1．130倍，该算法能有效地对低对比度的激光光斑图像进行增强。

英文摘要：

For the strong background and low contrast features of infrared image, an improved algorithm of laser spot image en- hancement based on particle swarm optimization is proposed. Combined with the idea of probability and statistics, the goal nee- ded to be enhanced is extracted after the third moment invariant image segmentation for laser spot image. The four mathematical models are established on the basis of γ correction algorithm and solved separately, an improved （a, b, γ） correction algorithm is proposed based on particle swarm optimization which is applied in the enhancement process of laser spot target. The experi- mental results show that the average intensity and entropy of far-infrared laser beam are increased by 5. 395 and 1. 326 times, and the average intensity and entropy of near-infrared laser beam are increased by 2. 458 and 1. 130 times, which can effectively en- hance the low-contrast laser spot image.