Fast and accuracy extraction of infrared target based on Markov random field

Infrared images are characterized by small signal-to-noise ratio (SNR) and low contrast thus making it much difficult to achieve accurate infrared target extraction. This paper proposes a fast and accurate segmentation approach to extract targets from an infrared image. First, the regions of interests (ROIs) which contain the entire targets region and a little background region are detected based on the variance weighted information entropy feature. Second, the infrared image is modeled by Gaussian Markov random field (MRF), and the ROIs are used as the target regions while the remaining region as the background to perform the initial segmentation. Finally, by searching solution space within the ROIs, the targets are accurately extracted by the energy minimization using the iterated condition mode (ICM) based on the fact that targets can only exist in ROIs. Because the iterated segmentation results are updated within the ROIs only, this coarse-to-fine extraction method can greatly accelerate the convergence speed and efficiently reduce the interference of the background clutter and noise. Experimental results of the real infrared images demonstrate that the proposed method can extract single and multiple infrared targets accurately and rapidly.