Infrared small target detection based on complex contourlet transform and principal component analysis

Aiming at the detection problem for dim target in infrared image that contains background interference and noise, a detection method was proposed based on complex contourlet transform and principal component analysis (PCA). Firstly, the background image separated from the original image by principal component analysis is subtracted from the original image. The residual image is denoised based on complex contourlet transform, then Top-hat operator is used to filter. So the preprocessed image is obtained. Secondly, the preprocessed image is segmented by using the threshold selection algorithm based on fuzzy Renyi entropy. Lots of experiments were done with infrared images including small targets and a comparison was made with the detection methods based on fast independent component analysis (FastICA) and based on NSCT. The experimental results show that this paper method is stronger in anti-noise performance and more superior in detection performance.