SAR image super-resolution based on TV-regularization using gradient profile prior

We propose an total variation (TV) regularization approach for the reconstruction of super-resolution synthetic aperture radar (SAR) image based on gradient profile prior. Then we design a super-resolution reconstruction algorithm via split Bregman iteration with the known degradation matrix. The evaluation index is tested on SAR images for objective assessment of the performance of SAR image super-resolution reconstruction. Experimental results show that the proposed split Bregamn super-resolution approach has good effect of noise suppression, while able to effectively maintain the SAR image content. Besides, the experimental results on real SAR scenes demonstrate its superiority to other super-resolution algorithms.