Pan-Sharpening with a Gradient Domain Guided Image Filtering Prior

Abstract

We develop a novel pan-sharpening method with a gradient domain guided image filtering (GGF) prior. A GGF prior is proposed to enforce effective fusion of panchromatic and multispectral images, which can promote multispectral image structures and suppress artifacts or noise. And the $l_{1}$ norm is accurately imposed on the GGF prior, which measures the error between panchromatic and multispectral images in image gradient domain. Then the proposed objective function is addressed by an efficient optimization scheme that iteratively alternates among GGF and $l_{1}$ norm approximations, and high resolution multispectral image reconstruction. Final experiments are provided to show the satisfactory performance of the proposed method in spatial and spectral fusion, and the proposed method outperforms several pan-sharpening methods in both subjective results and objective assessments.