论文标题
部分可观测时空混沌系统的无模型预测
Quaternion Optimized Model with Sparse Regularization for Color Image Recovery
论文作者
论文摘要
本文根据低级别的Quatenrion矩阵优化解决了颜色图像的完成问题，该问题的特征在于转化的域中稀疏的正则化。这项研究的灵感来自对以下事实的欣赏，即不同的信号类型（包括音频格式和图像）具有本质上稀疏各自基地的结构。由于可以在四元素域中整体处理颜色图像，因此我们描述了四个离散余弦变换（QDCT）域中颜色图像的稀疏性。另外，在季节矩阵完成问题中，与颜色图像固有的低级结构的表示是至关重要的问题。为了实现更高的低级别近似，在拟议的模型中采用了基于Quatenrion的截短核定标（QTNN）。此外，该模型通过基于算法的乘数（ADMM）的合格交替方向方法来促进。广泛的实验结果表明，与最先进的低级矩阵/Quaternion矩阵近似方法相比，所提出的方法可以产生非常出色的完成性能。
This paper addresses the color image completion problem in accordance with low-rank quatenrion matrix optimization that is characterized by sparse regularization in a transformed domain. This research was inspired by an appreciation of the fact that different signal types, including audio formats and images, possess structures that are inherently sparse in respect of their respective bases. Since color images can be processed as a whole in the quaternion domain, we depicted the sparsity of the color image in the quaternion discrete cosine transform (QDCT) domain. In addition, the representation of a low-rank structure that is intrinsic to the color image is a vital issue in the quaternion matrix completion problem. To achieve a more superior low-rank approximation, the quatenrion-based truncated nuclear norm (QTNN) is employed in the proposed model. Moreover, this model is facilitated by a competent alternating direction method of multipliers (ADMM) based on the algorithm. Extensive experimental results demonstrate that the proposed method can yield vastly superior completion performance in comparison with the state-of-the-art low-rank matrix/quaternion matrix approximation methods tested on color image recovery.
