Singular value decomposition ghost imaging.
Singular value decomposition ghost imaging.
复制标题
奇异值分解重影成像。
DOI：
10.1364/oe.26.012948
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发表时间：
2018-05
期刊：
影响因子：
3.8
通讯作者：
Xue Zhang;Xiangfeng Meng;Xiulun Yang;Yurong Wang;Yongkai Yin;Xianye Li;Xiang Peng;W. He;G. Dong;Hongyi Chen
中科院分区：
文献类型：
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作者：
Xue Zhang;Xiangfeng Meng;Xiulun Yang;Yurong Wang;Yongkai Yin;Xianye Li;Xiang Peng;W. He;G. Dong;Hongyi Chen
为了提高计算鬼像成像的逼真度，提出了奇异值分解鬼像成像（SVDGI）方法。对随机矩阵进行奇异值分解后，使奇异值矩阵的非零元素均为1.0，再进行奇异值逆变换得到测量矩阵。最后，原始物体可以通过将矩阵的转置乘以一系列收集的强度来重建。SVDGI使得能够使用远少于N个测量值来重建N像素图像，并且使用N个测量值来完美地重建原始对象。仿真和光学实验结果表明，SVDGI总是花费更少的时间来完成更好的工作。首先，它比GI和差分鬼像成像（DGI）快至少十倍，比伪逆鬼像成像（PGI）快几个数量级。其次，与GI方法相比，SVDGI方法的清晰度有了很大的提高，并且具有更强的鲁棒性，在噪声环境下也能得到更清晰的图像。
The singular value decomposition ghost imaging (SVDGI) is proposed to enhance the fidelity of computational ghost imaging (GI) by constructing a measurement matrix using singular value decomposition (SVD) transform. After SVD transform on a random matrix, the non-zero elements of singular value matrix are all made equal to 1.0, then the measurement matrix is acquired by inverse SVD transform. Eventually, the original objects can be reconstructed by multiplying the transposition of the matrix by a series of collected intensity. SVDGI enables the reconstruction of an N-pixel image using much less than N measurements, and perfectly reconstructs original object with N measurements. Both the simulated and the optical experimental results show that SVDGI always costs less time to accomplish better works. Firstly, it is at least ten times faster than GI and differential ghost imaging (DGI), and several orders of magnitude faster than pseudo-inverse ghost imaging (PGI). Secondly, in comparison with GI, the clarity of SVDGI can get sharply improved, and it is more robust than the other three methods so that it yields a clearer image in the noisy environment.