Singular value decomposition compressed ghost imaging based on non-negative constraints

Cheng Zhang; Jun Tang; Meiqin Wang; Qianwen Chen

doi:10.1117/12.2540201

14 August 2019 Singular value decomposition compressed ghost imaging based on non-negative constraints

Cheng Zhang, Jun Tang, Meiqin Wang, Qianwen Chen

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111793P (2019) https://doi.org/10.1117/12.2540201
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

Compressed ghost imaging can effectively enhance the quality of original image from far fewer measurements, but due to the non-negativity of the measurement matrix, the recover quality is thus limited. In this paper, singular value decomposition compressed ghost imaging is proposed; First, the singular value decomposition be used to decompose the measurement matrix, and then the optimized measurement matrix and measurements are used to recover the original image. Numerical experiments verify the superiority of our proposed singular value decomposition compression ghost imaging method.

Citation Download Citation

Cheng Zhang, Jun Tang, Meiqin Wang, and Qianwen Chen "Singular value decomposition compressed ghost imaging based on non-negative constraints", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793P (14 August 2019); https://doi.org/10.1117/12.2540201

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