9 January 1998 Iterative regularized mixed-norm image restoration algorithm
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This paper introduces a regularized mixed-norm image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed.A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals, and a function of the previous two functionals an the smoothing functional is utilized for determining the regularization parameter. The two parameters are chosen in such a way that the proposed functional is convex, so that a local minimizer becomes a global minimizer. The novelty of the proposed algorithm is than no knowledge of the noise distribution is required, and the relative contribution of the LMS, the LMF and the smoothing functional is adjusted based on the partially restored image.
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Min-Cheol Hong, Min-Cheol Hong, Tania Stathaki, Tania Stathaki, Aggelos K. Katsaggelos, Aggelos K. Katsaggelos, } "Iterative regularized mixed-norm image restoration algorithm", Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); doi: 10.1117/12.298374; https://doi.org/10.1117/12.298374

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