基于可变分裂和约束Optimization的快速帧图像反卷积
Fast Frame-Based Image Deconvolution Using Variable Splitting and Constrained Optimization
摘要：
我们提出了一种新的快速算法来解决基于帧的图像反卷积的标准公式之一：无约束最优化问题，涉及一个$\ell_2$数据保真度项和一个非光滑正则化。我们的方法是基于使用变量分裂，以获得一个等价的约束优化配方，然后与增广拉格朗日方法。所得到的算法有效地使用了正则化版本的数据保真度项的海森，从而利用二阶信息。一组图像去模糊基准问题的实验表明，我们的算法是明显快于以前的国家的最先进的方法。
英文摘要：
We propose a new fast algorithm for solving one of the standard formulations of frame-based image deconvolution: an unconstrained optimization problem, involving an $\ell_2$ data-fidelity term and a non-smooth regularizer. Our approach is based on using variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The resulting algorithm efficiently uses a regularized version of the Hessian of the data fidelity term, thus exploits second order information. Experiments on a set of image deblurring benchmark problems show that our algorithm is clearly faster than previous state-of-the-art methods.
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