Texture image prior for SAR image super resolution based on total variation regularization using split Bregman iteration

ABSTRACT

In this article, we propose a total variation (TV) regularization approach for the reconstruction of super-resolution synthetic aperture radar (SAR) image based on gradient profile prior or other texture image prior in the maximum a posteriori framework. We also design a novel super-resolution reconstruction algorithm via split Bregman iteration with the known degradation matrix, thereby enhancing the resolution of the SAR image. The parameter adaptation of the TV regularization is performed based on the high-resolution (HR) SAR image at each step. Several evaluation indices are tested on SAR images for objective assessment of the performance of SAR image super-resolution reconstruction. This computationally efficient algorithm is robust to noise in SAR scenes in HR image estimation. Experimental results show that the proposed split Bregman super-resolution approach can effectively avoid the speckle noise generated due to some strange textures and has good effect of noise suppression, while effectively maintaining the SAR image content, the structure of the SAR image is more apparent. Additionally, the experimental results on real SAR scenes also demonstrate the effectiveness of the proposed algorithm and demonstrate its superiority to other super-resolution algorithms.

Acknowledgements

We would like to thank Prof. Zhang and Xiaolin (2006), Li and Orchard (2001), and Giachetti and Asuni (2011) for providing their programs online. We are grateful for the financial support provided by the National Natural Science Foundation of China [Grant Numbers 11601506, 11201450, 91538112, and 41604157].

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

We are grateful for the financial support provided by the National Natural Science Foundation of China ​[Grant Numbers 11601506, 11201450, 91538112, and 41604157].