New background estimation and suppression algorithm via Zernike-facet model

Abstract

A new background estimation and suppression algorithm was presented. In the algorithm, targets and observing noises were considered as mixed interferences of the image background. With this situation, image background was estimated adaptively and then background suppression was done in order to improve the signal-to-noise ratio (SNR) of targets. In this algorithm, firstly, a Zernike-facet model of image background was built up. Secondly, the total least squares (TLS) method was used to solve parameters of the model. Finally, background estimation and suppression were done using the model and its parameters. Simulations and several experiments demonstrating the effectiveness of this proposed algorithm were reported. And results show that this algorithm can be effective to estimate background in mixed noise environment and can preserve detail information of targets and improve SNR of targets. As a result, detecting probability and false probability will be improved in next process for automatic target detection and tracking.