Region proposal patch-image model for infrared small target detection

Abstract

ABSTRACT The infrared patch-image (IPI) model has been proved to be very successful in infrared small target detection. However, the IPI model suffers from two major issues: abundant of residuals in the recovered target image and low detection speed caused by the large-size low-rank decomposition matrix. To mitigate these challenges, a novel infrared small target detection method called the region proposal patch-image (RPPI) model is proposed. The method contains two stages, where the first one is to generate region proposals (RPs) based on the contrast mechanism and the second one is to recover targets based on matrix decomposition. In the first stage, RPs containing interest targets are extracted in order to suppress background clutters and reduce the size of the decomposed matrix. In RP extraction, the contrast mechanism instead of the original convolutional neural network (CNN) is adapted due to lack of texture features for infrared small target and high computational complexity with CNN. In the second stage, considering the weakness of the correlation between RPs, a weighted nuclear norm (WNN) is utilized for avoiding relaxation of the sparsity too much, which can preserve targets and suppress the background simultaneously. Finally, accelerated proximal gradient (APG)-combined WNN is applied to solve this model. Experimental results on eight image groups with various kinds of targets and backgrounds demonstrate that the proposed method can effectively detect the target with faster speed, higher detection rate, and lower false alarm rate compared with other state-of-the-art methods.