High-order Markov Random Fields-Based Compressed Sensing for Multispectral Reconstruction

Remote sensing image reconstruction from sparsely observed data is eagerly demanded by the onboard imaging system to cut down data volume and maintain image quality. High-order Markov random fields describe the neighborhood constraints in the statistical form that could be integrated into compressed sensing to improve the reconstruction performance of remote sensing images. To this end, we built a new energy model with high-order model of Markov random fields to reconstruct remote sensing images from sparse signals. The split Bregman method is used to solve the problem. The proposed method is tested on some multispectral satellite imageries to make clear that it outweighs state-of-the-art methods such as Orthogonal Matching Pursuit, Group-based Sparse Representation, and total variation in maintaining fidelity and high visual details.