Multiresolution Analysis-Inspired Spatial and Spectral Details Preserved Model for Variational Pansharpening

Pansharpening, which is also known as the fusion of low-resolution multispectral (LRMS) and panchromatic (PAN) images, refers to producing a high-resolution multispectral (HRMS) image by preserving the spectral detail from the LRMS image while extracting the spatial detail from the PAN image. In this article, we revisit and novelly reinterpret the multiresolution analysis (MRA)-based pansharpening framework as the fusion framework of "spectral detail + spatial detail" and can obtain two alternative formulations of spectral detail and spatial detail, respectively, and hence propose a novel variational pansharpening method with MRA-inspired spatial and spectral details preserved model. First, the spatial degradation relationship between HRMS and LRMS is imposed as the spectral fidelity term. Second, based on the new reinterpretation of "spectral detail + spatial detail" of the MRA fusion framework, we propose to use the structure tensor to model the spatial detail image and propose a new structure tensor total variation (STV)-guided spatial detail preserved prior term. Moreover, to model the spectral detail image, we propose to impose the spectral detail preserved constraint between the two alternative formulations of spectral detail as the MRA-inspired spectral detail preserved prior term. Then, we optimize the proposed model via the alternating direction method of multipliers (ADMMs). Furthermore, various experimental results on the reduced-scale and full-scale datasets validate the superiority of the proposed method.