Compressive sensing measurement matrix construction based on improved size compatible array LDPC code

被引:17
作者
Yuan, Haiying [1 ]
Song, Hongying [1 ]
Sun, Xun [2 ]
Guo, Kun [1 ]
Ju, Zijian [1 ]
机构
[1] Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
基金
中国国家自然科学基金;
关键词
parity check codes; compressed sensing; matrix algebra; cyclic codes; image reconstruction; image coding; compressive sensing measurement matrix construction; improved size compatible array LDPC code; ISC-array LDPC code matrix; SC-array LDPC code matrix; shift index; repetitive row elimination; RIP well; quasicyclic structure; arbitrary code length; image reconstruction quality; SIGNAL RECOVERY; DETERMINISTIC CONSTRUCTIONS; BINARY;
D O I
10.1049/iet-ipr.2015.0117
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
ISC-array LDPC code matrix is evolved from SC-array LDPC code matrix to improve compressive sensing performance for large-size sparse signal. When q is a prime number, no repetitive row occurs in the shift index of SC-array LDPC code matrix, ISC-array LDPC code matrix performs comparably with SC-array LDPC code matrix. When q is a non-prime number, some repetitive rows will appear in the shift index of SC-array LDPC code matrix, which results in more 4-cycles and decreases the compressive sensing performance. ISC-array LDPC code matrix outperforms SC-array LDPC code matrix by effectively reducing or even eliminating the repetitive rows according to their distribution rule, 4-cycles are removed to the maximum extent. ISC-array LDPC code matrix qualifies for compressive sensing because of satisfying RIP well. It also has good quasi-cyclic structure and supports arbitrary code lengths. The simulations verify that the optimised ISC-array LDPC code matrix is advantageous in the image reconstruction quality, the robustness to noise performance and the algorithm complexity.
引用
收藏
页码:993 / 1001
页数:9
相关论文
共 26 条
[1]   Deterministic Construction of Binary, Bipolar, and Ternary Compressed Sensing Matrices [J].
Amini, Arash ;
Marvasti, Farokh .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2011, 57 (04) :2360-2370
[2]   Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery [J].
Applebaum, Lorne ;
Howard, Stephen D. ;
Searle, Stephen ;
Calderbank, Robert .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2009, 26 (02) :283-290
[3]   A new TwIST: Two-step iterative shrinkage/thresholding algorithms for image restoration [J].
Bioucas-Dias, Jose M. ;
Figueiredo, Mario A. T. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2007, 16 (12) :2992-3004
[4]   EXPLICIT CONSTRUCTIONS OF RIP MATRICES AND RELATED PROBLEMS [J].
Bourgain, Jean ;
Dilworth, Stephen ;
Ford, Kevin ;
Konyagin, Sergei ;
Kutzarova, Denka .
DUKE MATHEMATICAL JOURNAL, 2011, 159 (01) :145-185
[5]   Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].
Candès, EJ ;
Romberg, J ;
Tao, T .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509
[6]   Near-optimal signal recovery from random projections: Universal encoding strategies? [J].
Candes, Emmanuel J. ;
Tao, Terence .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (12) :5406-5425
[7]   Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors [J].
Chen, Fred ;
Chandrakasan, Anantha P. ;
Stojanovic, Vladimir M. .
IEEE JOURNAL OF SOLID-STATE CIRCUITS, 2012, 47 (03) :744-756
[8]  
Chen SSB, 2001, SIAM REV, V43, P129, DOI [10.1137/S003614450037906X, 10.1137/S1064827596304010]
[9]   Subspace Pursuit for Compressive Sensing Signal Reconstruction [J].
Dai, Wei ;
Milenkovic, Olgica .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2009, 55 (05) :2230-2249
[10]   Deterministic constructions of compressed sensing matrices [J].
DeVore, Ronald A. .
JOURNAL OF COMPLEXITY, 2007, 23 (4-6) :918-925