Pansharpening via Double Nonconvex Tensor Low-Tubal-Rank Priors

In this letter, based on the tensor representation modeling, we propose a novel and strict tensor-based pansharpening model via double nonconvex tensor low-tubal-rank (DNTLTR) priors for the fusion of low resolution multispectral (LRMS) and panchromatic (Pan) images to produce the high-resolution MS (HRMS) images. By modeling the MS image as a third-order tensor for better modeling its spatial-spectral structural correlations, we particularly exploit the tensor low-tubal-rank properties of HRMS as well as the difference of HRMS and Pan at the same time, and then propose a novel unified log tensor nuclear norm-based DNTLTR prior term. Moreover, for the spectral preservation of LRMS image, we also impose the spatial degradation-based spectral fidelity constraint between HRMS and LRMS. Then, we apply the alternating direction method of multiplier to optimize the proposed DNTLTR model. Finally, we show both the reduced-scale and full-scale fusion experiments to validate the effectiveness of DNTLTR visually and quantitatively.