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- W2056526029 abstract "In order to relax need of the optimal regularization parameter to be estimated, a cooperative recurrent neural network (CRNN) algorithm for image restoration was presented by solving a generalized least absolute deviation (GLAD) problem. This paper proposes a fast algorithm for solving a constrained l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm problem which contains the GLAD problem as its special case. The proposed iterative algorithm is guaranteed to converge globally to an optimal estimate under a fixed step length. Compared with the CRNN algorithm being continuous time, the proposed iterative algorithm has a fast convergence speed. Illustrative examples with application to image restoration show that the proposed iterative algorithm has a much faster convergence rate than the CRNN algorithm." @default.
- W2056526029 created "2016-06-24" @default.
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- W2056526029 date "2010-07-01" @default.
- W2056526029 modified "2023-09-23" @default.
- W2056526029 title "A fast algorithm for constrained GLAD estimation with application to image restoration" @default.
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- W2056526029 doi "https://doi.org/10.1109/wcica.2010.5554050" @default.
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