Second-Order Total Generalized Variation Regularization for Pansharpening

Abstract

Pansharpening can be regarded as an inverse problem, where a high-resolution multispectral (MS) image is estimated given a low-resolution MS image and a panchromatic (Pan) image. Considering the effectiveness of total generalized variation (TGV) to regularize ill-posed inverse problems, this letter proposes a variational model in accordance with the observation model based on the satellite imaging system and the second-order TGV. Furthermore, a primal–dual algorithm is adopted to resolve the variational model by splitting it into a sequence of simpler sub-problems, which leads to a more efficient algorithm compared to the other variational methods. In addition, the exploitation of TGV in the variational model allows the presence of the very fine details of the Pan image in the sharpened MS image whereas it is beyond the capabilities of the total variation (TV)-based models. The proposed algorithm is compared with some recent classical and variational pansharpening methods by experiments on two data sets at full and reduced resolution.