中文摘要：

低信噪比检测技术是实现红外自动目标识别的基本前提，其性能指标将直接决定系统探测距离的远近，是反映红外低可观测目标识别能力至关重要的一项核心技术。自适应背景估计方法是实现这一目标的有效途径。本文在重点论述几种背景估计常用技术的基础上，提出了红外极小目标的形态滤波优化改进算法，通过理论分析和实验检测表明：该算法简化了形态变换关系，优化了结构元构型，促进了滤波质量和运算速度的双向提高；保持了形态滤波算法有效保护信号特性的操作特点，并改善了它对杂波起伏不够敏感的固有缺陷，使其自适应背景感知能力更强；算法简洁紧凑，操作效率高；对复杂背景的低信噪比图像环境表现出良好的滤波性能和稳健的适应能力。

英文摘要：

Detecting target with low signal to noise ratio is a fundamental technique used for automatic target recognition （ATR） in infrared imagery, and its performance makes an ultimate impact on detection distance of a system. It is a leading key technique to indicate the ability of recognizing low-observable target in infrared imagery. Adaptive background estimation method is an efficient method to complete this task. On the basis of summarizing several current estimation techniques, a properly improved morphological filtering algorithm is proposed in this paper. Some theoretical analysis and experimental results show that this method is able to simplify operation of morphological conversion and to optimize formation of structuring elements. Consequently it can enhance the filtering result and accelerate the speed of operation as well. Moreover, it is capable of preserving the property to protect signal characteristics and improving its inherent limitation of insensitivity to noise fluctuation, therefore it is considered to have better ability of adaptive background perception in morphological filtering algorithm. In conclusion this method is concise and efficient. It provides good filtering results and robust adaptability to image targets with clutter background.