机构

• 自动化研究所 [2]
• 长春光学精密机械与物... [1]
• 中国科学院大学 [1]

采集方式

• OAI收割 [3]
• iSwitch采集 [1]

内容类型

• 会议论文 [2]
• 期刊论文 [2]

发表日期

• 2015 [1]
• 2013 [2]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 On Equivalence of l1 Norm Based Basic Sparse Representation Problems 会议论文  OAI收割 IEEE Conference of Signal Processing, Communications and Computing (ICSPCC), 2015, Ningbo, Zhejiang, China, September 19-22, 2015 作者:  Jiang, Rui;  Qiao, Hong;  Zhang, Bo;  Jiang R(姜锐) 收藏  |  浏览/下载：20/0  |  提交时间：2016/10/27 Equivalence  l1 norm regularization problem  l1 norm minimization problem  l1 norm constraint problem   Minimization of eigenvalues for some differential equations with integrable potentials 期刊论文  iSwitch采集 Boundary value problems, 2013, 卷号: 2013, 期号: 1 作者:  Meng,Gang 收藏  |  浏览/下载：38/0  |  提交时间：2019/05/09 Eigenvalue  Sturm-liouville equations  Minimization problem  Integrable potential  L1 ball   A fast convex conjugated algorithm for sparse recovery 期刊论文  OAI收割 NEUROCOMPUTING, 2013, 卷号: 115, 页码: 178-185 作者:  He, Ran;  Yuan, Xiaotong;  Zheng, Wei-Shi;  Ran He(赫然) 收藏  |  浏览/下载：22/0  |  提交时间：2015/08/12 Sparse representation  Half-quadratic minimization  L1 minimization   Image coding using wavelet-based compressive sampling (EI CONFERENCE) 会议论文  OAI收割 2012 5th International Symposium on Computational Intelligence and Design, ISCID 2012, October 28, 2012 - October 29, 2012, Hangzhou, China 作者:  Li J.;  Li J.;  Li J. 收藏  |  浏览/下载：49/0  |  提交时间：2013/03/25 In this paper  we proposed a novel coding scheme is proposed using wavelet-based CS framework for nature image. First  two-dimension discrete wavelet transform (DWT) is applied to a nature image for sparse representation. After multi-scale DWT  the low-frequency sub-band and high-frequency sub-bands are re-sampled separately. According to the statistical dependences among DWT coefficients  we allocate different measurements to low- and high-frequency component. Then  the measurements samples can be quantized. The quantize samples are entropy coded and forward correct coding (FEC). Finally  the compressed streams are transmitted. At the decoder  one can simply reconstruct the image via l1 minimization. Experimental results show that the proposed wavelet-based CS scheme achieves better compression performance against the relevant existing solutions.