Joint dictionary learning with ridge regression for pansharpening

Abstract

A novel pansharpening method is proposed for creating a fused image of high spatial and spectral resolutions through merging a panchromatic (PAN) image with a multispectral (MS) image. To replace the patch pairs sampled from the images directly as the dictionary pairs, a joint learning model is proposed to learn a pair of compact dictionaries. Meanwhile, instead of restricting the coding coefficients of low resolution (LR) MS and high resolution (HR) MS image patches to be equal, ridge regression model is employed to describe their relation. Then, the fused MS image is calculated by combining the mapped sparse coefficients and the dictionary for the HR MS image. By comparing with some well-known methods in terms of several universal quality evaluation indexes, the simulated experimental results demonstrate the superiority of our method.