Pansharpening Variational Model Based on Internal Adaptive Spatial Fidelity and External Deep-Driven Injection

Pansharpening is an image fusion technique that fuses the high spatial resolution of panchromatic (PAN) images and the rich spectral information of multispectral (MS) images to produce high-resolution multispectral (HRMS) images. The preservation of spatial details is crucial for enhancing the quality of the final results. However, existing detail extraction methods often fail to capture spatial information effectively. Most approaches rely only on internal details from the PAN image while overlooking external information, such as the deep-driven prior. Additionally, they struggle to establish an accurate relationship between the HRMS and PAN images, leading to spatial distortions. To address these issues, in this article, we propose a novel variational model based on double detail injection. Specifically, it integrates internal details from an adaptive spatial fidelity term and external details from a deep-driven injection term. Furthermore, an alternating direction method of multipliers (ADMM)-based algorithm is developed to efficiently solve the proposed model. The effectiveness of the proposed method is demonstrated through extensive experiments, showing superior performance compared to some existing pansharpening techniques.