Fast nonconvex nonsmooth minimization methods for image restoration and reconstruction
IEEE Transactions on Image Processing
SIAM Journal on Scientific Computing
Proximity algorithms for the L1/TV image denoising model
Advances in Computational Mathematics
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Image restoration problems are often solved by finding the minimizer of a suitable objective function consisting of a data-fitting term and a regularization term. In this paper, we consider the data-fitting term measured in the $\ell$1 norm to handle non-Gaussian additive noise and the regularization term given by the total variation (TV) to restore image edges. We propose a new algorithm for this image restoration problem by making use of new variables to modify the data-fitting term and the TV regularization term. An alternating minimization method based on the new formulation is employed to restore blurred and noisy images. Our experimental results show that the quality of restored images by the proposed method is competitive with those restored by the other tested methods. We also show the convergence of the alternating minimization algorithm and demonstrate that the proposed algorithm is very efficient.