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14 April 2022 An image quality improvement method for compressed sensing ghost imaging
Xinlong Mai, Longfei Yin, Guohua Wu, Bin Luo, Pengqi Yin
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Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 121780C (2022) https://doi.org/10.1117/12.2631906
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China
Abstract
The application of compressed sensing(GS) theory in ghost imaging reduces the sampling required for image reconstruction, thus improving the reconstruction efficiency. Due to its sparsity constraints on objects, the algorithm performs better on sparse and smooth images. Many studies have been carried out on sparse representation of objects and the solution of constraint equations. Different from the previous method, using GS method after orthogonalize the reference patterns as a pretreatment method to reconstruct image is proposed in this paper. We compare the simulation and experimental results of the original GS algorithm and the GS algorithm with using pretreated reference patterns. The results show that the pretreatment method improves the quality of reconstructed images in simulation and experimental. It is proved that the pretreatment method is a feasible method to improve the quality of reconstructed images.
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Xinlong Mai, Longfei Yin, Guohua Wu, Bin Luo, and Pengqi Yin "An image quality improvement method for compressed sensing ghost imaging", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 121780C (14 April 2022); https://doi.org/10.1117/12.2631906
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KEYWORDS
Signal to noise ratio
Image quality
Reconstruction algorithms
Compressed sensing
Image restoration
Telecommunications
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