The mixed Gaussian-Poisson noise is common in many systems. Sparse based methods are now considered state-of-the-art to reconstruct noisy images. Moreover, it’s gaining increasing attention to improve the sparse reconstruction methods with more image priors like nonlocal similarity. But most related work is aimed at single noise. And because of the definition of sparse representation, the image can only lie in a low dimensional subspace. The cosparse model is then proposed to move the emphasis on the number of zeros in the representation, thus enlarges the subspace’s dimensions. For the first time, we combine sparsity, nonlocal similarity and cosparsity to improve the reconstruction quality. Firstly, non local similarity is used as the melioration of sparse constraint. Then the data fidelity term and cosparsity constraint are added. The objective function is solved alternately and iteratively by IRLSM and GAP. Experimental results indicate that the proposed method can attain higher reconstruction quality.
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