Infrared small-dim target detection based on Markov random field guided noise modeling

Small target detection is one of the key techniques in infrared search and tracking applications. When small targets are very dim and of low signal-to-noise ratio, they are very similar to background noise, which usually causes high false alarm rates for conventional methods. To address this problem, we novelly treat the small-dim targets as a special sparse noise component of the complex background noise and adopt Mixture of Gaussians (MoG) with Markov random field (MRF) to model this problem. Firstly, the spatio-temporal patch image is constructed using several consecutive frames to utilize the temporal information of the image sequence. Then, the MRF guided MoG noise model under the Bayesian framework is proposed to model the small target detection problem. After that, by variational Bayesian, the small target component can be effectively separated from complex background noise. Finally, a simple adaptive segmentation method is used to extract small targets. Several series of experiments are done to evaluate the proposed method and the results show that the proposed method is robust for real infrared images with complex background.