Pansharpening Image Fusion Using Cross-Channel Correlation: A Framelet-Based Approach

This paper aims at developing a variational model for pansharpening image fusion. To resolve the ill-posedness of image fusion, we propose a new regularization technique that explores the cross-channel correlation of different spectral channels in wavelet tight frame (or framelet) domain. Besides using a regular cross-channel sparsity prior (Inverse Probl Imaging 7(3):777–794, 2013), the proposed model also makes efficient use of the panchromatic image as a guidance for image feature alignment. An ADMM-based iterative scheme is derived for solving the proposed model, and its performance is tested on several datasets including natural images, aerial images, and real multispectral satellite images. Numerical results suggest that the proposed approach works well on the testing datasets and outperforms some state-of-the-art algorithms in comparison.