Compressive ghost imaging (CSGI) combines structured illumination and a bucket detector for obtaining the light intensity signal from an unknown object. Light fluctuations are generated by few measurements and then an image is reconstructed using optimization algorithms such as compressive sensing (CS) by finding its sparse representation. The measured light fluctuations are not sensitive to scattering degradation. Consequently, an absorbing object completely embedded in a scattering media can be imaged. To speed up the sequential loading of illumination patterns on a digital micromirror device and achieve data compression, the sampling number should be reduced because more than 90% of the time in CS reconstruction is usually spent in getting a frame of image. In this study, we propose a novel strategy to realize a speedy and reliable reconstruction procedure for obtaining a high image quality by using prior knowledge during the acquisition of light intensity signal in CSGI. The prior knowledge is established by extracting the features of a local target area, which is designed by descattering of images with extra noise using fast Fourier single-pixel imaging. The proposed method facilitates a reliable image quality even under the reduction in the compression ratio, thereby overcoming the limitation of the dependence of sampling ratio on the image quality in CSGI.

中文翻译：

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由感兴趣区域引导的散射介质中的压缩鬼成像

压缩重影成像 (CSGI) 结合了结构化照明和桶检测器，用于从未知物体获取光强度信号。光波动是通过很少的测量产生的，然后使用优化算法（例如压缩感知 (CS)）通过找到其稀疏表示来重建图像。测得的光波动对散射退化不敏感。因此，可以对完全嵌入散射介质中的吸收物体成像。为了加快数字微镜设备上照明模式的顺序加载并实现数据压缩，应该减少采样次数，因为在 CS 重建中，90% 以上的时间通常用于获取一帧图像。在这项研究中，我们提出了一种新的策略，通过在 CSGI 中光强度信号的采集过程中使用先验知识来实现​​快速可靠的重建过程，以获得高质量的图像。先验知识是通过提取局部目标区域的特征来建立的，这是通过使用快速傅立叶单像素成像对具有额外噪声的图像进行去散射而设计的。所提出的方法即使在压缩比降低的情况下也有利于获得可靠的图像质量，从而克服了 CSGI 中采样率对图像质量依赖性的限制。它是通过使用快速傅立叶单像素成像对具有额外噪声的图像进行去散射而设计的。所提出的方法即使在压缩比降低的情况下也有利于获得可靠的图像质量，从而克服了 CSGI 中采样率对图像质量依赖性的限制。它是通过使用快速傅立叶单像素成像对具有额外噪声的图像进行去散射而设计的。所提出的方法即使在压缩比降低的情况下也有利于获得可靠的图像质量，从而克服了 CSGI 中采样率对图像质量依赖性的限制。