NSTMR: Super Resolution of Sentinel-2 Images Using Nonlocal Nonconvex Surrogate of Tensor Multirank

In this article, we address the super-resolution problems, which estimate the high-resolution multispectral images from the multispectral Sentinel-2 (S2) images with different resolutions. Since S2 images can be naturally represented by tensors, we reformulate the degradation process as the tensor-based form. Based on the degradation mechanism, we build a tensor-based optimization model for S2 images super-resolution problem, which fully exploits intrinsic nonlocal spatial similarity and global spectral redundancy. Specifically, the model consists of the data fidelity term and the low-multirank regularizer tailored to thoroughly mining the inherent spatial-nonlocal and spectral redundancy. Then, we develop an efficient alternating direction method of multipliers algorithm with theoretically guaranteed convergence to tackle the resulting tensor optimization problem. Numerical experiments including simulated and real data demonstrate that our method outperforms the competing methods visually and qualitatively.