Hyperspectral imagery denoising using multi-linear weighted nuclear norm minimization

Classical matrix-based denoising methods for hyperspectral imagery (HSI) may cause spatial and spectral distortion. To improve denoising performance, a multi-linear weighted nuclear norm minimization was proposed for HSI denoising. By considering spectral continuity and inter-dependency of three unfolding modes, a multi-linear rank was proposed to model the spatial and spectral nonlocal similarity. To make the proposed method more tractable, a variable splitting based technique was used to solve the optimization problem. Experiment results reveal that the proposed method outperforms state-of-the-art methods both visually and quantitatively.