We present here an effective scheme for image denoising based on total variation regularization. The proposed scheme allows to efficiently remove Poisson noise as well as Gaussian noise simultaneously with the help of a new kind of data fidelity term, suitable for the mixed Poisson-Gaussian noise model. The results show that the algorithm corresponding to our new scheme outperforms the existing methods for mixed Poisson-Gaussian noise removal.
In this paper, we present an effective algorithm for solving the Poisson-Gaussian total variation model. The existence and uniqueness of solution for the mixed Poisson-Gaussian model are proved. Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for mixed Poisson-Gaussion noise removal. Comparison with other existing and well-known methods is provided as well.
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