Spatial-spectral compressive sensing for hyperspectral images super-resolution over learned dictionary

This paper proposes a new hyperspectral images superresolution (HSI-SR) method based on compressive sensing (CS) theory, spatial sparsity and spectral similarity prior. First, according to sparsity and incoherence of CS theory, we propose a new dictionary learning method, ensuring that the learned dictionary not only has less dimensionality to speed up the sparse decomposition, but also satisfies sparsity well. Then, we introduce the spatial sparsity and spectral similarity regularizations into HSI-SR model, which can recover the spatial information effectively and preserve the spectral information well. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.