论文标题
基于卷积神经网络的高速计算幽灵成像具有压缩感测
High-speed computational ghost imaging with compressed sensing based on a convolutional neural network
论文作者
论文摘要
最近对计算幽灵成像（CGI）作为一种间接成像技术进行了深入研究。但是，CGI的速度无法满足实际应用的要求。在这里，我们提出了一种用于高速成像的新型CGI方案。在我们的情况下，常规的CGI数据处理算法优化为基于卷积神经网络（CNN）的新的压缩感应（CS）算法。 CS用于处理传统CGI设备收集的数据。然后，通过CNN训练处理后的数据以重建图像。实验结果表明，我们的方案可以产生与常规CGI相比采样少得多的高质量图像。此外，使用我们的方法和常规CS和深度学习（DL）重构的图像之间的详细比较表明，我们的方案表现优于常规方法，并达到更快的成像速度。
Computational ghost imaging (CGI) has recently been intensively studied as an indirect imaging technique. However, the speed of CGI cannot meet the requirements of practical applications. Here, we propose a novel CGI scheme for high-speed imaging. In our scenario, the conventional CGI data processing algorithm is optimized to a new compressed sensing (CS) algorithm based on a convolutional neural network (CNN). CS is used to process the data collected by a conventional CGI device. Then, the processed data are trained by a CNN to reconstruct the image. The experimental results show that our scheme can produce high-quality images with much less sampling than conventional CGI. Moreover, detailed comparisons between the images reconstructed using our approach and with conventional CS and deep learning (DL) show that our scheme outperforms the conventional approach and achieves a faster imaging speed.
