Infrared small target detection using tensor based least mean square

Infrared small target detection is an important topic in infrared image processing and pattern recognition. It plays an important role in reconnaissance, early warning system, aircraft tracking and missile guidance. Because of the low signal-to-noise ratio, small target size, obvious shape structure and texture information available, infrared small target detection is a very difficult task. Inspired by the observation that infrared small targets usually exist in image background and the image has matrix structure, we propose to observe the infrared small target in main view and develop a Tensor based least mean square (TLMS) method to detect infrared small target. In TLMS, the neighborhood of the target is firstly utilized to predict the gray value of the central pixel. The predicted background image and the difference image are derived from the principle of mean square minimum error. Finally, the target is obtained by performing the adaptive thresholding. We conduct the experiments on four datasets to compare the performance of TLMS with Max-median/Max-mean, Top-hat, left-TDLMS and right-TDLMS. The experimental results show that TLMS is an effective infrared small target detection method which can well suppress the background and highlight the infrared small targets.

Automated summary

Infrared small target detection is a challenging task due to low signal-to-noise ratio, small target size, and shape structure. The TLMS method proposed in this study effectively suppresses background noise and highlights small infrared targets. Experimental results demonstrate the effectiveness of TLMS in infrared small target detection.