Schmidt orthogonal optimization of measurement matrix

Xinyu He; Shengqin Bian; Lixin Zhang; Zhengguang Xu

doi:10.1117/12.2661967

28 December 2022 Schmidt orthogonal optimization of measurement matrix

Xinyu He, Shengqin Bian, Lixin Zhang, Zhengguang Xu

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Proceedings Volume 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022); 125063C (2022) https://doi.org/10.1117/12.2661967
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2022), 2022, Beijing, China

Abstract

Aiming at the disadvantage of poor stability of universal measurement matrix, this paper proposes an optimization method of measurement matrix, which is called Schmidt orthogonalization method. Row vectors are orthogonalized step by step to reduce the correlation between column vectors of measurement matrix. The simulation results show that under the same sampling rate, the one-dimensional signal reconstructed based on the optimized measurement matrix has higher reconstruction accuracy and quality, and the number of measurements required for accurate reconstruction is also reduced; the two-dimensional image reconstructed based on the optimized measurement matrix has higher signal-to-noise ratio.

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Xinyu He, Shengqin Bian, Lixin Zhang, and Zhengguang Xu "Schmidt orthogonal optimization of measurement matrix", Proc. SPIE 12506, Third International Conference on Computer Science and Communication Technology (ICCSCT 2022), 125063C (28 December 2022); https://doi.org/10.1117/12.2661967

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KEYWORDS

Matrices

Image restoration

Compressed sensing

Statistical analysis

Image quality

Sampling rates

Correlation coefficients

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