document: Iterative regularized least-mean mixed-norm image restoration We develop a regularized mixed-norm image restoration algorithm to deal with various types of noise . A mixed-norm functional is introduced , which combines the least mean square -LRB- LMS -RRB- and the least mean fourth -LRB- LMF -RRB- functionals , as well as a smoothing functional . Two regularization parameters are introduced : one to determine the relative importance of the LMS and LMF functionals , which is a function of the kurtosis , and another to determine the relative importance of the smoothing functional . The two parameters are chosen in such a way that the proposed functional is convex , so that a unique minimizer exists . An iterative algorithm is utilized for obtaining the solution , and its convergence is analyzed . The novelty of the proposed algorithm is that no knowledge of the noise distribution is required , and the relative contributions of the LMS , the LMF , and the smoothing functionals are adjusted based on the partially restored image . Experimental results demonstrate the effectiveness of the proposed algorithm
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