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Non-local low-rank tensor approximation has been developed as astate-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately,with more spectral bands for HSI, while the running time of these methodssignificantly increases, their denoising performance benefits little. In thispaper, we claim that the HSI underlines a global spectral low-rank subspace,and the spectral subspaces of each full band patch groups should underlie thisglobal low-rank subspace. This motivates us to propose a unifiedspatial-spectral paradigm for HSI denoising. As the new model is hard tooptimize, we further propose an efficient algorithm for optimization, which ismotivated by alternating minimization. This is done by first learning alow-dimensional projection and the related reduced image from the noisy HSI.Then, the non-local low-rank denoising and iterative regularization aredeveloped to refine the reduced image and projection, respectively. Finally,experiments on synthetic and both real datasets demonstrate the superiorityagainst the other state-of-the-arts HSI denoising methods.

Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising