摘要
强噪声背景下红外图像中弱小目标的检测一直是研究的重点和难点。根据弱小目标、背景干扰和噪声在红外图像中的差异,研究了三种低信噪比条件下红外图像中弱小目标的检测算法:小波变换、数学形态学、Top—hat算子,分别给出了处理的图像和相应的数据。仿真实验表明:这三种检测算法能十分有效地提高信噪比、增强目标、抑制背景杂波和去除噪声干扰,对信噪比约为2的弱小目标检测能得到很好的结果。三种算法所得结果一致,而且处理速度快,适合于实时图像处理和目标探测。
Detection of dim target in infrared image with strong noise and background is the emphasis and difficulty of target detection. Three algorithms of detecting dim target in infrared image with low signal-to-noise ratio according to the differences among dim target, background interference and noise are studied: wavelet transform, mathematical morphology, Top—hat operator. Processed images and corresponding data are also given respectively. The simulated experiment results with personal computer indicate that the algorithms are effective in improving the signal-to-noise ratio, enhancing the target, suppressing background clutter and diminishing the disturbing of the noise very effectively. A high performance is reached when signal-to-ratio is about 2.These methods which have the same results are adaptable to real—time image processing and target detection for their rapidity.
出处
《光学技术》
CAS
CSCD
2004年第3期337-339,342,共4页
Optical Technique
