Multiscale contrast enhancement method for small infrared target detection

Abstract

Infrared search and track (IRST) systems have been applied extensively in the civil and military fields. However, fast, effective, and robust dim and small infrared target detection remains a challenging research topic. Dim and small targets are easily obscured in different backgrounds or noises, which results in a high false alarm rate. A novel strategy is proposed for small infrared target detection to achieve a high true positive rate and robust detection. First, the prominent edge structures of the original image are obtained by constructing a structure tensor. Subsequently, a Gaussian bandpass filter with multiscale windows is used to obtain an infrared image that contains complex edges and small targets. An infrared image with an enhanced signal-to-clutter ratio is achieved by combining with the two methods, whereby the noise can be effectively reduced. Thereafter, candidate regions that contain the target and a smaller number of edges with higher brightness than that of the target are obtained using relaxed threshold segmentation. Finally, a Gaussian gradient contrast method is proposed in which the highlighted edges are removed so that only small infrared targets remain. Extensive experimental results demonstrate that the proposed method can achieve a significant optimization of the true positive and false alarm rates, as well as improved efficiency and robustness.