The method for constructing block sparse measurement matrix based on orthogonal vectors

Authors:
Ruizhen Zhao;Zhou Qin;Jinhui Tang
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing, China, Key Laboratory of Advanced Information Science and Network, Technology of Beijing, Beijing, China;Institute of Information Science, Beijing Jiaotong University, Beijing, China, Key Laboratory of Advanced Information Science and Network, Technology of Beijing, Beijing, China;School of Computer Science, Nanjing University of Science and Technology, Nanjing, China
Venue:
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Year:
2012

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Abstract

Compressive sensing is a new way of information processing which recover the original signal through acquiring much fewer measurements with a measurement matrix. The measurement matrix has an important effect in signal sampling and reconstruction algorithm. However, there are two main problems in currently existing matrices: the difficulty of hardware implementation and high computation complexity. In this paper, we proposed a class of highly sparse and deterministic scrambled block measurement matrices based on orthogonal vectors (SBOV). It could improve sensing efficiency and reduce computation complexity. Those matrices constructed by the proposed method only need very little memory space and they could be easily implemented in hardware due to their simple entries. Some experiments show the better imaging performance comparable to scrambled block Hadamard matrix (SBH) and dense partial Hadamard matrix. SBOV matrices are simpler and sparser than SBH matrix.