Wang Zhirun, Zhao Wenjing, Zhai Aiping, He Peng, Wang Dong
Opt Express. 2021 May 10;29(10):15463-15477. doi: 10.1364/OE.422636.
For an orthogonal transform based single-pixel imaging (OT-SPI), to accelerate its speed while degrading as little as possible of its imaging quality, the normal way is to artificially plan the sampling path for optimizing the sampling strategy based on the characteristic of the orthogonal transform. Here, we propose an optimized sampling method using a Deep Q-learning Network (DQN), which considers the sampling process as decision-making, and the improvement of the reconstructed image as feedback, to obtain a relatively optimal sampling strategy for an OT-SPI. We verify the effectiveness of the method through simulations and experiments. Thanks to the DQN, the proposed single-pixel imaging technique is capable of obtaining an optimal sampling strategy directly, and therefore it requires no artificial planning of the sampling path there, which eliminates the influence of the imperfect sampling path planning on the imaging performance.
对于基于正交变换的单像素成像（OT-SPI），为了在尽可能少地降低成像质量的同时加快其速度，通常的方法是根据正交变换的特性人工规划采样路径以优化采样策略。在此，我们提出一种使用深度Q学习网络（DQN）的优化采样方法，该方法将采样过程视为决策，将重建图像的改进视为反馈，以获得针对OT-SPI的相对最优采样策略。我们通过仿真和实验验证了该方法的有效性。得益于DQN，所提出的单像素成像技术能够直接获得最优采样策略，因此无需在此进行采样路径的人工规划，这消除了不完美的采样路径规划对成像性能的影响。
