Alternating proximal algorithm is presented for L1/TVp (0 < p < 1) nonconvex variational model, which typically outperforms popular models with convex variational models in restoring sparse images with corruption by the impulse noise or other outliers. Numerical experiments are reported to illustrate the effectiveness of this algorithm.
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