%0 Journal Article
%T 基于加权星图稀疏正则化的图像压缩感知重构
%A 谢中华
%A 马丽红
%J 工程科学与技术
%D 2018
%R 10.15961/j.jsuese.201700156
%X 中文摘要: 为了更有效地表达图像的高阶稀疏结构，提出基于图稀疏正则化的压缩感知重构算法，通过图论方法描述图像稀疏系数间的相关性。首先，采用图结构化稀疏度量表征图像的非局部相似性，并化简稀疏系数的完全图结构为仅与均值节点连接的星图结构，以实现更高效的稀疏表达；然后，通过加权范数的形式体现稀疏系数的不同重要性，达到自适应恢复的目的。进一步，提出求解星图稀疏模型的近似消息传递算法，通过引入辅助变量，使得权值参数和稀疏系数的优化问题更易求解。实验结果表明，所提出的算法在客观质量和主观质量上优于其他基于非局部稀疏模型的重构算法，验证了星图稀疏模型的有效性。</br>Abstract:In order to more effectively represent the higher-order sparse structure of images,a novel compressed sensing (CS) reconstruction algorithm based on the graph sparsity regularization was proposed in this paper.The graph theory method was introduced for describing the dependency of sparse coefficients.First,the nonlocal similarity of images was constrained to be graph-structured sparse.To achieve more efficient sparse representation,the structure of sparse coefficients was simplified from the complete graph structure to a star graph of which the coefficients are only connected with the mean node.Second,for obtaining the adaptive reconstruction,the weighted norm was utilized to reflect the different significances of sparsity coefficients.A numerical optimization algorithm was then proposed to solve the star graph structured reconstruction model by the approximate message passing (AMP) algorithm.Finally,the weight parameters and sparse coefficients were estimated easily by introducing auxiliary variables.Experiments results showed that,compared with several image reconstruction algorithms based on nonlocal sparse models,the proposed method presented competitive results in terms of both objective and subjective quality,which validated the effectiveness of the star graph structured model.
%K 压缩感知 非局部相似性 星图稀疏 加权范数 近似消息传递&lt
%K /br&gt
%K compressed sensing nonlocal similarity star graph structured sparsity weighted norm approximate message passing
%U http://jsuese.ijournals.cn/jsuese_cn/ch/reader/view_abstract.aspx?file_no=201700156&flag=1