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首页> 外文会议>International conference on graphic and image processing >Image Deblurring Based on Nonlocal Regularization With a Non-Convex Sparsity Constraint
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Image Deblurring Based on Nonlocal Regularization With a Non-Convex Sparsity Constraint

机译：基于非局部稀疏约束的非局部正则化图像去模糊

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摘要

In recent years, nonlocal regularization methods for image restoration (IR) have drawn more and more attention due to the promising results obtained when compared to the traditional local regularization methods. Despite the success of this technique, in order to obtain computational efficiency, a convex regularizing functional is exploited in most existing methods, which is equivalent to imposing a convex prior on the nonlocal difference operator output. However, our conducted experiment illustrates that the empirical distribution of the output of the nonlocal difference operator especially in the seminal work of Kheradmand et al. should be characterized with an extremely heavy-tailed distribution rather than a convex distribution. Therefore, in this paper, we propose a nonlocal regularization-based method with a non-convex sparsity constraint for image deblurring. Finally, an effective algorithm is developed to solve the corresponding non-convex optimization problem. The experimental results demonstrate the effectiveness of the proposed method.
机译：近年来，与传统的局部正则化方法相比，由于获得了令人鼓舞的结果，用于图像恢复（IR）的非局部正则化方法引起了越来越多的关注。尽管这项技术取得了成功，但为了获得计算效率，在大多数现有方法中都采用了凸正则化函数，这等效于在非局部差分算子输出上施加凸先验。但是，我们进行的实验表明，非局部差分算子输出的经验分布，尤其是在Kheradmand等人的开创性工作中。应该以极重尾分布而不是凸分布为特征。因此，在本文中，我们提出了一种基于非局部正则化的，具有非凸稀疏约束的图像去模糊方法。最后，开发了一种有效的算法来解决相应的非凸优化问题。实验结果证明了该方法的有效性。

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