Nonseparable sparsity based hyperspectral compressive sensing

摘要: Accurate reconstruction of hyperspectral image(HSI) from a few random sampled measurements is crucial for hyperspectal compressive sensing. The underlying sparsity HSI one factor reconstruction. However, the s-parsity unknown in reality and varied with different noise. To address this problem, novel nonseparable based sensing(NSHCS) method proposed study. We use empirical Bayes to deduce non-separable constraint. correlation among sparse coefficients signal modeled implicitly by Since parameters constraint are determined noise term together, learned can be adaptive With constraint, NSHCS reconstruct precisely. Experimental results demonstrate superiority over several state-of-the-art sensing methods