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Research of incoherence rotated chaotic measurement matrix in compressed sensing

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Abstract

Measurement matrix construction is the hot issue of compressed sensing. How to construct a measurement matrix of good performance and easy hardware implementation is the main research problem in compressed sensing. In this paper, we present a novel simple and efficient measurement matrix named Incoherence Rotated Chaotic (IRC) matrix. We take advantage of the well pseudorandom of chaotic sequence, introduce the concept of the incoherence factor and rotation, and adopt QR decomposition to obtain the IRC measurement matrix which is suited for sparse reconstruction. The IRC matrix satisfies the Restricted Isometry Property criterion in sparse reconstruction and has a smaller RIP ratio. Simulations demonstrate IRC matrix has better performance than Gaussian random matrix, Bernoulli random matrix, Fourier matrix and can efficiently work on both natural image and remote sensing image. The peak signal-to-noise ratios of reconstructed images using IRC matrix are improved at 1.5 dB to 2.5 dB at least.

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Acknowledgments

This paper was supported by the Foundation Research Funds for the central Universities (204201kf0242, 204201kf0263), National Natural Science Foundation of China (61572372, 41271398), Shanghai Aerospace Science and Technology Innovation Fund Projects (SAST201425).

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan, 430079, China

    Shihong Yao, Tao Wang, Weiming Shen, Pan Shaoming & Yanwen Chong

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  1. Shihong Yao
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  2. Tao Wang
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  3. Weiming Shen
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  4. Pan Shaoming
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  5. Yanwen Chong
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Correspondence to Yanwen Chong.

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Yao, S., Wang, T., Shen, W. et al. Research of incoherence rotated chaotic measurement matrix in compressed sensing. Multimed Tools Appl 76, 17699–17717 (2017). https://doi.org/10.1007/s11042-015-2953-2

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  • DOI: https://doi.org/10.1007/s11042-015-2953-2

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