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Fast Image Recovery Using Variable Splitting and Constrained Optimization
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DOI：10.1109/TIP.2010.2047910.png)
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摘要
En 中文
We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an data-fidelity term and a nonsmooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called alternating direction method of multipliers, for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.
Keyword:
Augmented Lagrangian
compressive sensing
convex optimization
image reconstruction
image restoration
inverse problems
total variation
variable splitting
wavelets
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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