In this work, 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 processas the tensorbased form. Based on the degradation mechanism, we build a tensor-based optimization model for S2 images super-resolution problem which fully exploitsintrinsicnonlocal spatial similarity and global spectral redundancy. Specifically, the model consists of the data fidelity term and the low-multi-rank regularizer tailored to thoroughly mining the inherent spatial-nonlocal and spectral redundancy. Then we develop an efficientalternating direction method of multipliersalgorithmwith theoretically guaranteed convergenceto tackle the resulting tensor optimization problem. Numerical experiments including simulated and real data demonstrate that our method outperforms the competing methods visually and qualitatively.
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