A robust variational approach to super-resolution with nonlocal TV regularisation term

Abstract

Multi frame super-resolution (SR) reconstruction algorithms make use of complimentary information among low-resolution (LR) images to yield a high-resolution (HR) image. Inspired by recent development on the video denoising problem, we propose a robust variational approach for SR-based on a constrained variational model that uses the nonlocal total variation (TV) as a regularisation term. In our method, a weighted fidelity term is proposed to take into account inaccurate estimates of the registration parameters and the point spread function. Moreover, we introduce the nonlocal TV as a regularisation term in order to take into account complex spatial interactions within images. In this way, important features and fine details are enhanced simultaneously with noise reduction. Furthermore, an alternative nonlocal TV regularisation is proposed based on a better weight function which integrates gradient similarity and radiometric similarity. Experiments show the effectiveness and practicability of the proposed method.

Keywords:

• super-resolution
• robust data fidelity
• nonlocal total variation
• regularisation.

The authors wish to thank the National Natural Science Foundation of China (no. 60972001), the National Key Technologies R&D Program of China (no. 2009BAG13A06) and the Program Sponsored for Scientific Innovation Research of College Graduate in Jiangsu Province (no. CXZZ_0163). The authors also thank the anonymous reviewers for their constructive and valuable comments, which helped in improving the presentation of our work.