Infrared Small Target Detection Based on LWT and Recursive  Minimum Within-cluster Absolute Difference

Aiming at the detection problem of dim target in infrared image that contains background interference and noise, a detection method is proposed based on lifting wavelet transform and recursive minimum within-cluster absolute difference. Firstly, the image is preprocessed. The original image is denoised based on lifting wavelet transform, then the background of denoised image is suppressed by Top-hat operator. At the same time, the background of original image is suppressed by Top-hat operator, then Top-hat operator is further used after the residual image is denoised. Addition of the above-mentioned two resultant images gives the preprocessed image. Secondly, the preprocessed image is segmented using the threshold selected by recursive minimum within-cluster absolute difference. Lots of experiments are done with infrared images including small targets, and a comparison is made with the detection methods for infrared small target based on morphological filter and based on wavelet and morphology. The results show that the signal-to-noise ratio of the suggested method is improved, and detection rate increases by 15% and 10%, respectively.