常用的基于高斯光束特性的激光光斑图像处理算法，处理远场光斑图像会丢失部分能量较低的光斑数据，致使处理出的光斑能量密度低端精度不能达到0.01μJ/cm2的需求。为了得到更精确的远场激光光斑数据信息，提出了基于噪声特性的激光光斑图像自动阈值处理算法。该算法在分析系统噪声特性的基础上，依据3σ原则确定图像提取阈值进行光斑图像处理。通过试验验证了该算法既能够有效抑制系统噪声，又能够改善光斑图像的处理质量，恢复光斑图像丢失的数据信息，使光斑能量密度低端达到探测需求。结果表明，基于噪声特性的光斑图像处理算法能够有效提高远场激光光斑的处理精度，更适用于远场激光光斑图像的处理。

When processing far-field spot images with the commonly used laser spot image processing algorithm based on Gaussian beam characteristics, the data about lower energy is lost so that the processing accuracy of spot energy density at lower-end can not reach 0.01μJ/cm2 in demand. In order to get more accurate far-field laser spot data, an automatic threshold processing algorithm was proposed based on noise characteristics of the laser spot image. After analyzing the noise characteristics, the image extraction threshold was determined based on the 3σ principles and the light spot image was processed. Experiments show that the algorithm can effectively restrain system noise, improve the light spot image processing quality, restore missing data information of the spot image, and make spot energy density low enough meet detection requirements. The results show that, the processing algorithm of light spot images based on the noise characteristics can effectively improve the processing accuracy of the far field laser facula, and is more suitable for the far field laser spot image processing.