Pan-GGF: A probabilistic method for pan-sharpening with gradient domain guided image filtering

Abstract

Abstract In this paper, we develop a novel probabilistic pan-sharpening method with gradient domain guided image filtering (named Pan-GGF). We employ gradient domain guided image filtering (GGIF) to enforce effective spatial fusion of panchromatic and multispectral images, and the proposed scheme shows better spatial and spectral fusion than other fusion methods, such as projection substitution, detail injection and weighted combination models. A maximum a posterior (MAP) formulation is then presented with imposing l1 norm priors on the errors between panchromatic and multispectral images in both image and gradient domains, and the proposed objective function is addressed through an efficient optimization scheme that alternates between auxiliary variables approximation and high resolution multispectral images reconstruction. Numerous experiments are performed to demonstrate the satisfactory performance of the proposed method in spatial and spectral fusion, and the proposed method outperforms several classical and state-of-the-art pan-sharpening methods in both subjective results and objective assessments.