鬼影成像
奇异值分解
计算机科学
图像质量
采样(信号处理)
基质(化学分析)
比例(比率)
算法
领域(数学)
奇异值
计算复杂性理论
质量(理念)
光学
计算机视觉
图像(数学)
物理
数学
材料科学
特征向量
滤波器(信号处理)
复合材料
量子力学
纯数学
作者
Hong Wang,Xiaoqian Wang,Chao Gao,Xuan Li,Yu Wang,Huan Zhao,Zheng Yao
标识
DOI:10.1016/j.optlastec.2023.110196
摘要
High-quality computational ghost imaging under low sampling rates has always attracted much attention and plays an important role in practical applications. In this paper, a novel optical field optimization method based on multi-scale light fields singular value decomposition which can greatly reduce the number of computational ghost imaging measurements is proposed. The computational ghost imaging measurement matrix is derived from the components obtained by singular value decomposition of self-designed special measurement matrices. When the measurement matrix is fully sampled, high-quality reconstructed image can be obtained. Similarly, when the measurement matrix is under-sampled, it is still possible to obtain high-quality reconstructed image and show the performance of multi-resolution imaging. Simulation and experimental results show that our method can obtain high-quality computational ghost imaging, even at low sampling rates, and as the number of splicing matrices increases, the number of measurements is further reduced.
科研通智能强力驱动
Strongly Powered by AbleSci AI