Variational pansharpening based on high-pass injection fidelity with local dual-scale coefficient estimation

Pansharpening is proposed to fuse a high spatial resolution panchromatic (PAN) image and a low spatial resolution multispectral image to generate a high resolution multispectral (HRMS) image with both high spatial resolution and high spectral resolution. Many previous studies have focused only on the global or local relationship between PAN and the corresponding HRMS images in the intensity or gradient domains. However, we observe that the relationship between PAN and HRMS images can be better explored in the high-pass domain through adaptive coefficients. We propose high-pass injection fidelity (HPIF) with adaptive local dual-scale coefficient (LDSC) estimation, which can adequately model the complex relationship between PAN and HRMS images in the high-pass domain and efficiently preserve spatial details. In addition, we propose a new spectral correction term to assist HPIF in avoiding spectral distortion. Specifically, we first compute corresponding LDSC from every input, and then the LDSC assists HPIF to extract spatial and spectral information. Finally, we add a total variation term to assist our proposed HPIF and spectral correction terms, which together make the final pansharpening model. We optimize our model by an alternating direction method of multipliers-based algorithm. Through comparative experiments with existing state-of-the-art pansharpening methods on QuickBird, GaoFen, and WorldView, we demonstrate the superiority of our proposed method in terms of both quantitative metrics and subjective visual effects.