Infrared small target detection using reinforced MSER-induced saliency measure

Abstract

This paper presents a robust scheme to extract weak small target under intricate backgrounds. Firstly, the maximally stable extremal regions (MSER) algorithm is employed to seek extremal regions whose size and shape are consistent with the definition of small target and whose gray level is relatively stable. Then, in view of the fact that small targets are relatively sparse defect areas in the whole image, the MSER-induced global saliency measure (MGSM) is developed to reduce regular backgrounds and enhance target signal. Meanwhile, based on the characteristics of small targets with compact gray levels and a certain contrast with its surrounding background, the MSER-induced local saliency measure (MLSM) is designed to reliably enlarge the target signal and remove strong clutter interferences. Finally, the reinforced MSER-induced saliency measure (RMSM) defined by fusing MGSM and MLSM can successfully eliminate complex backgrounds and highlight real targets. Results demonstrate that this method has superiority in enhancing dim target against various backgrounds and has strong robustness to different target shapes and sizes.