Yu Wen-Kai, Zhu Chen-Xi, Li Ya-Xin, Wang Shuo-Fei, Cao Chong
Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement of Ministry of Education, School of Physics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2021 Nov 13;21(22):7559. doi: 10.3390/s21227559.
Ghost imaging is an indirect optical imaging technique, which retrieves object information by calculating the intensity correlation between reference and bucket signals. However, in existing correlation functions, a high number of measurements is required to acquire a satisfied performance, and the increase in measurement number only leads to limited improvement in image quality. Here, inspired by the gradient descent idea that is widely used in artificial intelligence, we propose a gradient-descent-like ghost imaging method to recursively move towards the optimal solution of the preset objective function, which can efficiently reconstruct high-quality images. The feasibility of this technique has been demonstrated in both numerical simulation and optical experiments, where the image quality is greatly improved within finite steps. Since the correlation function in the iterative formula is replaceable, this technique offers more possibilities for image reconstruction of ghost imaging.
鬼成像（Ghost imaging）是一种间接光学成像技术，它通过计算参考信号和桶信号之间的强度相关性来获取物体信息。然而，在现有的相关函数中，需要大量测量才能获得令人满意的性能，并且测量次数的增加只会导致图像质量的有限提升。在此，受人工智能中广泛使用的梯度下降思想启发，我们提出了一种类梯度下降鬼成像方法，以递归方式朝着预设目标函数的最优解移动，从而能够高效地重建高质量图像。该技术的可行性已在数值模拟和光学实验中得到验证，在有限步骤内图像质量得到了极大提高。由于迭代公式中的相关函数是可替换的，该技术为鬼成像的图像重建提供了更多可能性。
