End-to-end shallow network for variational pansharpening

Pansharpening aims to fuse the geometry of a high-resolution panchromatic image with the color information of a low-resolution multispectral image to generate a high-resolution multispectral image. Classical variational methods are more interpretable and flexible than pure deep learning approaches, but their performance is limited by the use of rigid priors. In this paper, we efficiently combine both techniques by introducing a shallow residual network to learn the regularization term of a variational pansharpening model. The proposed energy includes the classical observation model for the multispectral data and a constraint to preserve the geometry encoded in the panchromatic. The experiments demonstrate that our method achieves state-of-the-art results.