Abstract
Abstract Restoring Poissonian images is particularly considered due to astronomical and medical applications in recent years. In this paper, we propose a novel noise removal method for restoration of hyperspectral images corrupted by Poisson noise, based on spectral unmixing technique. We formulate Poissonian hyperspectral image problem as an optimization problem where the cost function consists of three terms. The likelihood of the observation with Poisson distribution is used as the data-fidelity term. The total variation of the abundance for piecewise smooth information and the l1-norm of the abundance for sparse information are introduced as two regularization terms. Finally, the optimization problem is effectively solved by alternating direction optimization algorithm. Therefore, the hyperspectral image can be well restored after Poisson noise is successfully removed. The experiment results show that the proposed method has a better performance than current Poissonion hyperspectral image denoising methods, in terms of both image quality and computation time.
Published Version
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