红外图像中弱小目标的检测一直是图像处理领域的热点和难点。介绍了一种基于小波变换和Context模型空间自适应滤波的红外弱小目标检测算法。该方法首先利用小波滤波器抑制大部分背景杂波，然后采用基于Context模型的空间自适应滤波器对高频系数做进一步处理，提高信噪比，最后采用基于Bayes分类算法的迭代门限进行图像分割。实验结果表明，与高通滤波相比，该方法能够更有效地检测出弱小目标。

Small target detection from infrared image(IR) has been a hot and difficult issue in image progressing filed. In this paper, a detection method based on wavelet transform and Context model spatial adaptive filter is presented. Firstly, a wavelet filter is used to suppress the background clutter, then a Context-based adaptive filter is utilized in the further processing of the wavelet detail coefficients, which can improve the signal to noise ratio(SNR) greatly and preserve the candidate targets information effectively. Finally, the candidate target is segmented from background using iterative threshold based on Bayes classification algorithm. Experimental results show that it can detect the target more effectively compared with the high-pass filtering.