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First order roughness penalty for hyperspectral image denoising

In this paper, a new denoising method for hyperspectral images using First Order Roughness Penalty (FORP) is proposed. The proposed algorithm is applied in the wavelet domain to exploit the multiresolution analysis property of wavelets and thus improving the denoising results. Stein's Unbiased Risk Estimator (SURE) is used to choose the tuning parameters automatically. The experimental results show improvements for simulated data sets based on Signal to Noise Ratio (SNR) and visually for real data set.