Hyperspectral image denoising using a new linear model and Sparse Regularization

This paper deals with hyperspectral image reconstruction using a new linear model and Sparse Regularization (SR). The new model is based on Principal Components (PCs) and wavelets. Since the hyperspectral PCs are not spatially sparse, wavelet is applied to get spatially sparse representation. Sparse regularization is used to recover the corrupted signal. The regularization parameter is chosen by Stein´s Unbiased Risk Estimator (SURE). The results show improvements for simulated data sets compare to other denoising methods based on Signal to Noise Ratio (SNR). In addition, the methods are applied on a real noisy data set, and the results of the new method demonstrate visual improvement. The proposed approach is automatic, fast and has the ability to be applied on very large data sets.