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，其上的光电成像探测器易受敌方激光干扰产生局部饱和甚至损伤，导致目标识别失效。在实际中，激光干扰功率和光斑位置等因素均时刻发生变化，导致激光对每帧图像的干扰效果也不尽相同，因此，动态的衡量激光干扰效果具有重要意义。提出了一种基于光斑特征和图像特征的无参考动态激光干扰图像评估算法，通过加速分隔测试特征(FAST)算法提取特征点，并运用特征点匹配确定图像的目标区域，在该目标区域利用饱和像素数和光斑相对位置变化表示光斑特征对图像质量的影响，利用图像中特征点和边缘点分布特性以及边缘清晰度表示图像特征的变化，将5个评价因子相乘，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。基于提出的无参考动态特征评估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不同功率、方位激光的干扰效果，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 photoelectric imaging detector in the imaging systems is easy to be disturbed from opponents and this leads to errors of the recognition. In practice, the laser power and the spot position change momentarily and this leads to different laser-dazzling effects in every frame. Therefore, dynamic assessments of laser-dazzling effects must be better understood. A new no-reference dynamic feature metric (DFM) assessment algorithm based on the character of the laser spot and image feature is proposed. The features from accelerated segment test (FAST) algorithm is used to extract feature points of the target image and disturbed image. The target area is obtained via feature-point matching, and the characteristic of the laser spot reflects the image quality through the saturated pixel number and spot position in the target area. After that, the distribution of the feature points, edges and edge definition reflect the varieties of the image feature. The normalized DFM is obtained via product of the five factors above. The luminance imaging experiment is performed for the targets by utilizing the laser active imaging system. In the experiment, the disturbed images of different disturbing powers and different spot positions are obtained. The proposed DFM algorithm is used to evaluate the newly obtained laser-dazzling images, and the results show that the DFM gives a more reasonable evaluation result for different laser-dazzling images. The evaluation results reflect the invalidation of the target identification algorithm objectively.