Compressive Sensing (CS) has proved to be an effective theory in the field of image acquisition. However, in order to distinguish the difference between the measurement matrices, the CS imaging system needs to have a higher signal sampling accuracy. At the same time, affected by the noise of the light path and the circuit, the measurements finally obtained are noisy, which directly affects the imaging quality. We propose a dual-optical imaging system that uses the bidirectional reflection characteristics of digital micromirror devices (DMD) to simultaneously acquire CS measurements and images under the same viewing angle. Since deep neural networks have powerful modeling capabilities, we trained the filter network and the reconstruction network separately. The filter network is used to filter the noise in the measurements, and the reconstruction network is used to reconstruct the CS image. Experiments have proved that the method we proposed can filter the noise in the sampling process of the CS system, and can significantly improve the quality of image reconstruction under a variety of algorithms.

中文翻译：

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基于光路的自适应压缩传感成像系统

压缩感知 (CS) 已被证明是图像采集领域的有效理论。然而，为了区分测量矩阵之间的差异，CS成像系统需要具有更高的信号采样精度。同时，受光路和电路噪声的影响，最终得到的测量结果有噪声，直接影响成像质量。我们提出了一种双光学成像系统，该系统利用数字微镜器件 (DMD) 的双向反射特性在相同视角下同时获取 CS 测量值和图像。由于深度神经网络具有强大的建模能力，我们分别训练了滤波器网络和重建网络。滤波器网络用于过滤测量中的噪声，重建网络用于重建CS图像。实验证明，我们提出的方法能够滤除CS系统采样过程中的噪声，在多种算法下都能显着提高图像重建质量。