Gaussian ScaleSpace Enhanced Local Contrast Measure for Small Infrared Target Detection Robust smalltarget detection plays an important role in the infrared (IR) search and track system, but it is still a challenge to detect small IR target under complex background. In this letter, an effective method inspired by the scalespace theory and the contrast mechanism of the human vision system is proposed. First, Gaussian scalespace (GSS) of an IR image is constructed by the convolution of a variablescale Gaussian function. Second, the gray features of the local image can be directly represented by downsampling in a scale image, and enhanced local contrast measure (ELCM) is defined to enhance small target and suppress complex background. Then, the saliency map is obtained by using maxpooling operation, and an adaptive threshold is adapted to segment real targets. Experimental results on a test set with three real IR sequences demonstrate that the proposed method has a good performance in target enhancement and background suppression, and shows strong robustness under complex background. Especially, the proposed method has high computational efficiency, which can improve detection speed.

WaveletBased Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background Waveletbased Contourlet transform (WBCT) is a typical Multiscale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.