Reweighted Total Variation Regularization Based on Split Bregman in Synthetic Aperture Imaging Radiometry

Synthetic aperture imaging radiometers (SAIRs) are a powerful tool for high-resolution imaging. The reconstruction process from the visibility function to the brightness temperature map in SAIRs has been demonstrated to be an ill-posed inverse problem. Although the existing regularization methods can effectively overcome the ill-condition, there are still large residual errors and oscillation ripples, especially at the edges of the reconstructed map. In this letter, a reweighted total variation (RTV) method is presented to reconstruct the brightness temperature map in SAIRs and preserve the edge information of the map without windowing. A split Bregman iteration algorithm is used to optimize the RTV regularization model and increase the calculation speed. Numerical simulation experiments are carried out to verify the effectiveness and show the performance of the proposed method.