SAR image reconstruction based on Bayesian matching pursuit
基于贝叶斯匹配追踪的SAR图像重构

Based on sparse learning and CS theory, this paper proposed a new SAR image reconstruction method. The process of image reconstruction was treated as a linear regression problem and the image to reconstruction was the unknown parameters of the regression model. It used Gaussian mixture parameters to predefine the prior conditional density of the unknown parameters in order to confine the sparsity. A set of model could be achieved that could be used to reconstruct the image in sense of MMSE. When the hyperparameters were unknown, the method based on EM could be used to estimate. Simulation results indicate that the Bayesian matching pursuit based method can get a precisely reconstructed image and the details can be preserved.