激光主动成像系统通常用于重要区域监视和危险目标识别，当该系统受到敌方激光干扰时，其成像质量将会下降。由于目标、成像系统和干扰源的相对位置，以及干扰源的干扰功率处于时刻变化中，导致不同时刻的干扰效果不尽相同，如何衡量一段时间内激光对成像系统的干扰效果成为一个难题。提出了一种基于连续多帧图像动态特征变化的无参考激光干扰评估算法，在图像目标区域内分析单帧干扰图像中特征点的匹配准确率和空间变化率以及多帧图像特征点变化的准确性、空间性、结构性差异、频率和显著性特征，最终得到归一化的评估指标。利用激光主动成像识别系统对设定目标进行照明成像识别实验，采集不同干扰功率和干扰方位的激光干扰图像。基于提出的特征点动态性算法对获得的连续多帧激光干扰图像进行评估，结果表明该算法能够准确评价一段持续干扰过程中不同功率、方位的激光干扰效果，客观反映光斑遮盖下自动目标识别算法的失效程度。

Laser active imaging systems are usually used in important region surveillance and dangerous target identification. However, the imaging system is easy to be disturbed and this leads to the distortion of the images. The positions of the target, imaging system, interference and the laser power change momentarily, so the dazzling effect is different in every frame. Therefore, how to evaluate the influence of the laser on imaging systems must be better understood. A new no-reference image quality assessment algorithm based on the dynamic features of consistent multi-frame images is proposed, which is called feature-point dynamic (FPD). The feature-point matching correlation and area diversification of one frame are calculated in the target region. The difference of correlation, area distribution, structure, frequency and salience of multi-frame images are also compared in the target region. The normalized results are obtained via product of these factors above. The luminance imaging experiment is performed for the targets by utilizing the laser active imaging system. The disturbed images of different disturbing powers and different spot positions are obtained. The proposed FPD algorithm is used to evaluate the laser-dazzling images, and the results show that the FPD gives a more reasonable evaluation for multi-frame laser-dazzling images in the period of time. The evaluation results reflect the invalidation of the target identification algorithm objectively.