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.

中文翻译：

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基于DQN的单像素成像

对于基于正交变换的单像素成像（OT-SPI），为了在降低成像质量的同时尽可能快地提高速度，通常的方法是人为地规划采样路径，以根据采样特性来优化采样策略。正交变换。在这里，我们提出了一种使用深度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，并将重构图像的改进视为反馈，从而为OT- SPI。我们通过仿真和实验验证了该方法的有效性。多亏了DQN，提出的单像素成像技术能够直接获得最佳的采样策略，因此无需人工规划那里的采样路径，