@article{_k-svd_2012,
	title = {{基于卷积稀疏编码和K}-{SVD联合字典的稀疏表示}},
	volume = {34},
	issn = {1001-506X},
	url = {https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKgchrJ08w1e7fm4X_1ttJAnAS3RqJs7fTLG4JYBGpLXicCp6K4xSLSO1XkE7mom_P_dPqvGOjgoI&uniplatform=NZKPT},
	abstract = {针对现有稀疏表示算法存在字典单一、编码冗余的缺点,从人类视觉感知系统层次处理特性出发,依据神经元侧抑制与竞争机理,构建了基于卷积稀疏编码和K-奇异值分解(K-singular value decomposition,K-SVD)的联合字典。在此基础上提出结合卷积匹配追踪和正交匹配追踪算法对图像进行分层稀疏表示。实验结果表明联合字典能够自适应匹配图像中的边缘、斑块、纹理等特征,与单独的卷积字典和K-SVD冗余字典相比,稀疏表示能力更强。},
	language = {zh},
	number = {7},
	urldate = {2023-07-02},
	journal = {系统工程与电子技术},
	author = {{练秋生} and {韩冬梅}},
	year = {2012},
	note = {Core: 北大核心, EI, CSCD},
	keywords = {\#Convolutional, \#Joint, /unread, combined dictionary, convolutional matching pursuit, lateral inhibition and competition, sparse representation, visual perception, ⭐⭐⭐⭐, 侧抑制与竞争, 卷积匹配追踪, 稀疏表示, 联合字典, 视觉感知},
	pages = {1493--1498},
}

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