Image regularization with higher-order morphological gradients
Image regularization with higher-order morphological gradients
复制标题
使用高阶形态梯度进行图像正则化
DOI：
10.1109/eusipco.2015.7362698
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
M. Nakashizuka
中科院分区：
文献类型：
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作者：
M. Nakashizuka
本文提出了一种基于形态学特征的图像先验算法用于图像恢复。所提出的先验是形态梯度及其高阶扩展的和。将形态梯度定义为图像的膨胀与侵蚀之差，并得到梯度的离散模量。为了抑制恢复图像中出现的伪影，我们引入了高阶形态梯度。提出的先验正则化问题被简化为约束最小化问题。为了将次梯度法应用于这一问题，我们推导了所提先验的次梯度。我们将提出的先验算法应用于图像去噪，并证明了提出的高阶形态梯度先验算法能够抑制阶梯伪影。并与全变差图像进行了比较。
In this paper, we propose an image prior based on morphological image features for image recovery. The proposed prior is obtained as the sum of morphological gradient and its higher-order extensions. The morphological gradient is defined as the difference between dilation and erosion of an image and obtains a discretized modulus of gradient. In order to suppress artifacts appear in the recovered image, we introduce higherorder morphological gradients. The regularization problem with the proposed prior is reduced to a constrained minimization problem. In order to apply the subgradient method to this problem, we derive the subgradient of the proposed priors. We apply the proposed prior to image denoising and demonstrate that the proposed higher-order morphological gradient prior is capable to suppress staircase artifacts. Comparison with the total variation image prior is also demonstrated.