Adaptive compressive ghost imaging based on wavelet trees and sparse representation.
Adaptive compressive ghost imaging based on wavelet trees and sparse representation.
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DOI：
10.1364/oe.22.007133
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发表时间：
2014-03
期刊：
影响因子：
3.8
通讯作者：
Wen-Kai Yu;Ming-fei Li;Xu-Ri Yao;Xue-feng Liu;Ling-An Wu;G. Zhai
中科院分区：
文献类型：
--
作者：
Wen-Kai Yu;Ming-fei Li;Xu-Ri Yao;Xue-feng Liu;Ling-An Wu;G. Zhai
压缩感知是一种通过寻找图像的稀疏表示，只需少量测量就可以几乎完美地重建图像的理论。然而，大图像所消耗的计算时间可能是几个小时或更多。在这项工作中，我们从理论上和实验上证明了一种方法，结合了自适应计算鬼成像和压缩传感的优点，我们称之为自适应压缩鬼成像，从而可以显着减少任何图像大小所需的重建时间和测量。该技术可用于提高所有计算重影成像协议的性能，特别是在测量超弱或噪声信号时，并可扩展到任何波长的成像应用。
Compressed sensing is a theory which can reconstruct an image almost perfectly with only a few measurements by finding its sparsest representation. However, the computation time consumed for large images may be a few hours or more. In this work, we both theoretically and experimentally demonstrate a method that combines the advantages of both adaptive computational ghost imaging and compressed sensing, which we call adaptive compressive ghost imaging, whereby both the reconstruction time and measurements required for any image size can be significantly reduced. The technique can be used to improve the performance of all computational ghost imaging protocols, especially when measuring ultra-weak or noisy signals, and can be extended to imaging applications at any wavelength.