IR small target detection based on human visual attention using pulsed discrete cosine transform

Abstract

Detection of small targets in an infrared (IR) image with high reliability is very important for defence systems. Small targets in an IR image are defined as salient features which attract the attention of human visual system. In this study, a robust method for detection of small targets in an IR image is proposed based on HV attention. In this method, first, the Gaussian-like feature maps are extracted from the original image. Then, saliency maps (SMs) are created based on pulsed discrete cosine transform, in which the target is salient and background clutter is suppressed. Finally, to increase the contrast between target and background clutter and to raise robustness of this method against false alarms, SMs are fused adaptively. Experiments are carried out on the data set including real-life IR images with small targets as well as various and complicated backgrounds. Qualitative and quantitative assessments show that the proposed method can detect small targets in IR image with high reliability and is more effective compared with other methods based on HV attention. Therefore, it can be used in many applications for detection of small targets in IR image with minimum false alarms.