Structure Tensor-Driven Block-Based Adaptive Variational Pansharpening

Pansharpening, as a widely used technique, plays a crucial role in the field of remote sensing image processing. In this letter, we propose a novel structure tensor-driven block-based variational pansharpening model with adaptive coefficients. First, a structure descriptor derived from the structure tensor of the panchromatic (PAN) image is integrated into the regularization term. It can not only effectively capture the edge information of the PAN image, but also contribute to better preserving the spatial details of the PAN image in the fused product. Furthermore, we partition the degraded PAN image and the upsampled multispectral (MS) image into several equal-sized blocks and utilize a regression-based approach to calculate the adaptive coefficients within each block. As a result, a more accurate constraint relationship between the PAN image and the high-resolution MS image can be established. Then, by incorporating the regularization term and the fidelity term, the proposed variational model is formulated. An explicit finite difference scheme is employed to efficiently solve the gradient descent flow of the proposed model. Experiments conducted on different datasets demonstrate that the proposed pansharpening method outperforms the state-of-the-art techniques in both qualitative and quantitative evaluations.