针对存在背景干扰和噪声情况下的红外弱小目标检测问题，提出一种基于无下采样contourlet变换(NSCT)和独立分量分析(ICA)的检测方法。首先原始图像减去通过快速ICA分离出的背景图像，再经NSCT去噪，接着利用新型Top-hat变换滤波得到预处理图像；然后采用基于类内方差及背景与目标面积差的阈值选取方法来分割预处理图像。针对红外小目标图像进行了大量实验，并和基于快速ICA、基于NSCT的红外目标检测方法进行了比较，结果表明所提出的方法抗噪性强，具有更为优越的检测性能。

Aiming at the detection problem for dim target in infrared image that contains background interference and noise, a detection method for dim target is proposed based on nonsubsampled contourlet transform (NSCT) and independent component analysis (ICA). Firstly, the background image separated from the original image by fast independent component analysis is subtracted from the original image. The residual image is denoised based on nonsubsampled contourlet transform and the new Top-hat transform is used as a filter, thus the preprocessed image is obtained. Then, the preprocessed image is segmented by the threshold selection algorithm based on the within-class variance and area difference between background and target. Lots of experiments are done with infrared images including small targets and a comparison is made with the detection methods of infrared target based on fast independent component analysis and nonsubsampled contourlet transform. The experimental results show that the suggested method is stronger in anti-noise performance and more superior in detection performance.