This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based ...This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.展开更多
颜色迁移是组织病理学图像颜色预处理中的重要环节.为了解决颜色迁移过程中某些重要结构颜色改变的问题,在保结构颜色迁移(structure-preserving color normalization,SPCN)算法基础上融合聚类过程,并结合稀疏非负矩阵分解(sparse non-n...颜色迁移是组织病理学图像颜色预处理中的重要环节.为了解决颜色迁移过程中某些重要结构颜色改变的问题,在保结构颜色迁移(structure-preserving color normalization,SPCN)算法基础上融合聚类过程,并结合稀疏非负矩阵分解(sparse non-negative matrix factorization,SNMF)提出K均值稀疏非负矩阵分解基组合(K-means and SNMF basis combination,KSBC)算法.首先通过K均值算法对图像聚类,根据聚类中心识别细胞结构;然后求解稀疏非负矩阵分解模型得到染色基和结构矩阵,根据聚类结果对结构矩阵和染色基准确组合.KSBC算法承袭了SPCN算法的特性,又能灵活地迁移和保留原图像结构颜色.在组织病理学图像数据库中进行对比实验,KSBC算法在图像质量评估指标上优于直方图匹配,Reinhard,Macenko,SPCN和高阶矩算法,并提高残差神经网络的泛化性能.展开更多
基金Supported by The Featured Innovation Projects of the General University of Guangdong Province(2023KTSCX096)The Special Projects in Key Areas of Guangdong Province(ZDZX1088)Research Team Project of Guangdong University of Education(2024KYCXTD018)。
文摘This paper explores the recovery of block sparse signals in frame-based settings using the l_(2)/l_(q)-synthesis technique(0<q≤1).We propose a new null space property,referred to as block D-NSP_(q),which is based on the dictionary D.We establish that matrices adhering to the block D-NSP_(q)condition are both necessary and sufficient for the exact recovery of block sparse signals via l_(2)/l_(q)-synthesis.Additionally,this condition is essential for the stable recovery of signals that are block-compressible with respect to D.This D-NSP_(q)property is identified as the first complete condition for successful signal recovery using l_(2)/l_(q)-synthesis.Furthermore,we assess the theoretical efficacy of the l2/lq-synthesis method under conditions of measurement noise.
文摘颜色迁移是组织病理学图像颜色预处理中的重要环节.为了解决颜色迁移过程中某些重要结构颜色改变的问题,在保结构颜色迁移(structure-preserving color normalization,SPCN)算法基础上融合聚类过程,并结合稀疏非负矩阵分解(sparse non-negative matrix factorization,SNMF)提出K均值稀疏非负矩阵分解基组合(K-means and SNMF basis combination,KSBC)算法.首先通过K均值算法对图像聚类,根据聚类中心识别细胞结构;然后求解稀疏非负矩阵分解模型得到染色基和结构矩阵,根据聚类结果对结构矩阵和染色基准确组合.KSBC算法承袭了SPCN算法的特性,又能灵活地迁移和保留原图像结构颜色.在组织病理学图像数据库中进行对比实验,KSBC算法在图像质量评估指标上优于直方图匹配,Reinhard,Macenko,SPCN和高阶矩算法,并提高残差神经网络的泛化性能.
