σ δ quantization for compressive sensing

被引:10
作者
Boufounos, Petros [1 ]
Baraniuk, Richard G. [1 ]
机构
[1] Rice Univ, ECE Dept, Houston, TX 77251 USA
来源
WAVELETS XII, PTS 1 AND 2 | 2007年 / 6701卷
关键词
sigma delta; quantization; compressive sensing;
D O I
10.1117/12.734880
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Compressive sensing is a new data acquisition technique that aims to measure sparse and compressible signals at close to their intrinsic information rate rather than their Nyquist rate. Recent results in compressive sensing show that a sparse or compressible signal can be reconstructed from very few measurements with an incoherent, and even randomly generated, dictionary. To date the hardware implementation of compressive sensing analog-to-digital systems has not been straightforward. This paper explores the use of Sigma-Delta quantizer architecture to implement such a system. After examining the challenges of using Sigma-Delta with a randomly generated compressive sensing dictionary, we present efficient algorithms to compute the coefficients of the feedback loop. The experimental results demonstrate that Sigma-Delta relaxes the required analog filter order and quantizer precision. We further demonstrate that restrictions on the feedback coefficient values and stability constraints impose a small penalty on the performance of the Sigma-Delta loop, while they make hardware implementations significantly simpler.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Pooled Testing with Compressive Sensing
    Yang, Jing
    Prater-Bennette, Ashley
    [J]. BIG DATA III: LEARNING, ANALYTICS, AND APPLICATIONS, 2021, 11730
  • [42] Invertible Image Compressive Sensing
    Sun, Bingfeng
    Zhang, Jian
    [J]. PATTERN RECOGNITION AND COMPUTER VISION, PT IV, 2021, 13022 : 548 - 560
  • [43] Authenticated Compressive Sensing Imaging
    Wu, Tao
    Ruland, Christoph
    [J]. 2017 INTERNATIONAL SYMPOSIUM ON NETWORKS, COMPUTERS AND COMMUNICATIONS (ISNCC), 2017,
  • [44] Compressive Wireless Pulse Sensing
    Chen, Hsieh-Chung
    Gulati, Harnek
    Kung, H. T.
    Teerapittayanon, Surat
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COLLABORATION TECHNOLOGIES AND SYSTEMS, 2015, : 5 - 11
  • [45] GENERALIZED TENSOR COMPRESSIVE SENSING
    Li, Qun
    Schonfeld, Dan
    Friedland, Shmuel
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME 2013), 2013,
  • [46] Holographic reconstruction by compressive sensing
    Leportier, T.
    Park, M-C
    [J]. JOURNAL OF OPTICS, 2017, 19 (06)
  • [47] COMPRESSIVE SENSING OF LIGHT FIELDS
    Babacan, S. Derin
    Ansorge, Reto
    Luessi, Martin
    Molina, Rafael
    Katsaggelos, Aggelos K.
    [J]. 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, 2009, : 2337 - +
  • [48] COMPRESSIVE SENSING OF A SUPERPOSITION OF PULSES
    Hegde, Chinmay
    Baraniuk, Richard G.
    [J]. 2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, 2010, : 3934 - 3937
  • [49] Evolutionary algorithm for compressive sensing
    Chakraborty, Uday K.
    [J]. INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL, 2015, 9 (01) : 61 - 70
  • [50] LENSLESS IMAGING BY COMPRESSIVE SENSING
    Huang, Gang
    Jiang, Hong
    Matthews, Kim
    Wilford, Paul
    [J]. 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2013, : 2101 - 2105