Variational Pansharpening by Exploiting Cartoon-Texture Similarities

Pansharpening aims to fuse a multispectral (MS) image with low spatial resolution and a panchromatic (PAN) image with a high-spatial resolution to produce an image with both high spectral and high spatial resolution. In this study, we propose a variational pansharpening method by exploiting cartoon-texture similarities. After decomposition of the PAN image, the cartoon component always contains the global structure information, while the texture component includes the locally patterned information. This enables that the fused high-spatial resolution MS image can preserve the global and local spatial details (e.g., high-order information) well after leveraging the similarities of cartoon and texture components from PAN and MS images. To explore such cartoon-texture similarities, we describe cartoon similarity as gradient sparsity, formulated as a reweighted total variation term. Meanwhile, we use group low-rank constraint for texture similarity that is presented as repetitive texture patterns. By incorporating a data fidelity term for preserving the spectral information on the basis that the down-sampled fused MS image is consistent with the MS image, we further formulate pansharpening as an optimization problem and solve it efficiently using the alternative direction multiplier method. Extensive experiments have been conducted on a series of satellite data sets, and we also carry out a simulated vegetation coverage change experiment to verify the efficiency of the proposed method in remote sensing. The qualitative and quantitative results demonstrate that our method outperforms the state-of-the-art pansharpening methods in terms of both visual effect and objective metrics.