Hyperspectral Image Superresolution Based on Double Regularization Unmixing

Abstract

This letter proposes a novel double regularization unmixing-based method for hyperspectral image (HSI) superresolution. The proposed cost function contains two data-fidelity terms, the endmember regularization term and the abundance regularization term. Since the double regularization unmixing terms are able to exploit the spatial structure information of endmember and abundance, respectively, the nonnegative factorization (spectral unmixing) error is minimized. As a result, the performance of the proposed HSI superresolution method can be enhanced in terms of noise suppression and the special structure information preservation of reconstruction images. Finally, the associated optimization problem is effectively solved by an alternating direction optimization algorithm. Simulation results illustrate that the proposed method has a better performance than the state-of-the-art methods in terms of both visual effectiveness and quality indices.