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A comparative investigation on the use of compressive sensing methods in computational ghost imaging

机译:计算鬼映像中压缩传感方法使用的比较研究

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Usually, a large number of patterns are needed in the computational ghost imaging (CGI). In this work, the possibilitiesto reduce the pattern number by integrating compressive sensing (CS) algorithms into the CGI process are systematicallyinvestigated. Based on the different combinations of sampling patterns and image priors for the L1-norm regularization,different CS-based CGI approaches are proposed and implemented with the iterative shrinkage thresholding algorithm.These CS-CGI approaches are evaluated with various test scenes. According to the quality of the reconstructed imagesand the robustness to measurement noise, a comparison between these approaches is drawn for different sampling ratios,noise levels, and image sizes.
机译:通常,计算鬼映像(CGI)中需要大量模式。在这项工作中,可能性通过将压缩感测(CS)算法集成到CGI过程中来减少图案编号,系统地是系统的调查。基于L1-Norm正规的采样模式和图像前导者的不同组合,用迭代收缩阈值算法提出和实现了不同的基于CS的CGI方法。这些CS-CGI方法通过各种测试场景进行评估。根据重建图像的质量并且对测量噪声的鲁棒性,为不同的采样比绘制这些方法之间的比较,噪声水平和图像尺寸。

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