1. 福州大学 物理与信息工程学院,福建,福州,350000
2. 闽南师范大学 机构 物理与信息工程学院,福建,漳州,363000
纸质出版:2020
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杨晶晶, 陈颖频. 基于二阶全变分与Lp伪范数的图像解模糊研究[J]. 微电子学与计算机, 2020, 37(9): 18-23.
YANG Jing-jing, CHEN Ying-pin. Research on image deblurring based on second order total variation and Lp pseudo-norm[J]. 2020, 37(9): 18-23.
各向异性全变分模型广泛应用于图像解模糊
然而
传统的各向异性全变分模型存在以下两个缺点:首先
传统各向异性全变分模型假设图像为分片常数且只考虑了图像一阶梯度的稀疏约束导致恢复的图像存在严重的阶梯伪影效应; 其次传统全变分模型采用L1范数来刻画图像梯度的稀疏性,然而L1范数刻画图像的稀疏性的能力有限,为改进这两个缺点,本文提出一种结合了二阶梯度稀疏约束和Lp伪范数新的去模糊方法,引入二阶全变分是为了适应图像的先验项从而去除阶梯效应,引入Lp伪范数是为了更准确刻画图像的稀疏性,为求解模型,采用交替乘子迭代法将模型分解为若干个去耦合的子问题.通过实验展现本文提出方法的效果.结果显示相比于本文提到的其他四种模型,本文提出的模型具有更优的效果。
Anisotropic total variation (ATV) regularization is widely used in image deblurring applications. However
the traditional ATV model suffers from at least two limitations. Firstly
the traditional ATV model assumes the image to be piecewise constant and only considers the sparsity of the first-order image gradients
resulting in staircase artifacts. Secondly
the traditional ATV model employs the L1-norm to depict the sparsity of image gradients. Nonetheless
the L1-norm has a limited capability of depicting the sparsity of sparse variables. To overcome the two challenges
a new deblurring model is presented via the second-order gradient sparse constraint and Lp-pseudo-norm shrinkage. The second-order total variation is incorporated for fitting the image prior term and relieving the staircase artifacts. The Lp-pseudo-norm shrinkage is employed to depict sparse variables precisely. The alternating direction method of multipliers is employed to decompose the presented model into several decoupled subproblems. Finally
experiments are carried out to show the performance of the proposed method. The results show that the proposed method outperforms the other deblurring models mentioned in this article.
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