In this paper, a fast algorithm for image deblurring is discussed. The algorithm is based on generalized inverse iteration, and linearized Bregman iteration for the basis pursuit problem. Numerical experiments show that the chaotic algorithm for image restoration is effective and efficient.
In this paper, a reweighted l1 minimization algorithm for compressed sensing is proposed. The algorithm is based on
generalized inverse iteration and linearized Bregman iteration, which is used for the weighted l1 minimization problem min u∈Rn {||u||ω : Au = f }. Numerical experiments confirm that the reweighted algorithm for signal restoration is effective and competitive to the recent state-of-the-art algorithms.
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