INFRARED BACKGROUND CLUTTER SUPPRESSION ALGORITHM OF ADAPTIVE REGULARIZATIONBASED ON MRF
基于MRF的自适应正则化红外背景杂波抑制算法

Aiming at the difficulty in detecting infrared(IR) dim small target under strong background clutter, the process of background suppression was attributed to the reconstruction of the target signal from the original IR dim small target image. Thus, a novel adaptive regularization filtering algorithm based on Markov random field(MRF) model was proposed. In our algorithm, the prior probability model of the IR dim small image was established by MRF, and a new potential function was introduced according to the roughness of the IR image. On this basis, the adaptive anisotropic filtering effect for background clutter suppression was realized by regularizing the process of background clutter suppression with MRF. Theoretical analysis and experimental results show that this algorithm can adjust the operator adaptively according to the local texture distribution character of the image. Thus, the target was enhanced and strong background clutter was eliminated. The proposed algorithm can improve the signal-to-noise ratio (SNR) of the image obviously with the advantage of its logical structure simple to be implemented in real-time system.