Noise-robust and data-efficient compressed ghost imaging via the preconditioned S-matrix method

被引:0
|
作者
Zhu, Xiaohui [1 ]
Tan, Wei [1 ]
Huang, Xianwei [1 ]
Liang, Xiaoqian [1 ]
Zhou, Qi [1 ]
Bai, Yanfeng [1 ]
Fu, Xiquan [1 ]
机构
[1] Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China
基金
中国国家自然科学基金;
关键词
PERFORMANCE ANALYSIS; LIDAR;
D O I
10.1364/JOSAA.535343
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The design of the illumination pattern is crucial for improving imaging quality of ghost imaging (GI). The S-matrix is an ideal binary matrix for use in GI with non-visible light and other particles since there are no uniformly configurable beam-shaping modulators in these GI regimes. However, unlike widely researched GI with visible light, there is relatively little research on the sampling rate and noise resistance of compressed GI based on the S-matrix. In this paper, we investigate the performance of compressed GI using the S-matrix as the illumination pattern (SCSGI) and propose a post-processing method called preconditioned S-matrix compressed GI (PSCSGI) to improve the imaging quality and data efficiency of SCSGI. Simulation and experimental results demonstrate that compared with SCSGI, PSCSGI can improve imaging quality in noisy conditions while utilizing only half the amount of data used in SCSGI. Furthermore, better reconstructed results can be obtained even when the sampling rate is as low as 5%. The proposed PSCSGI method is expected to advance the application of binary masks based on the S-matrix in GI. (c) 2024 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
引用
收藏
页码:2090 / 2098
页数:9
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