Wang Ping, Wang Lishun, Qiao Mu, Yuan Xin
Opt Lett. 2023 Sep 15;48(18):4813-4816. doi: 10.1364/OL.499735.
Coded aperture compressive temporal imaging (CACTI) aims to capture a sequence of video frames in a single shot, using an off-the-shelf 2D sensor. This approach effectively increases the frame rate of the sensor while reducing data throughput requirements. However, previous CACTI systems have encountered challenges such as limited spatial resolution and a narrow dynamic range, primarily resulting from suboptimal optical modulation and sampling schemes. In this Letter, we present a highly efficient CACTI system that addresses these challenges by employing precise one-to-one pixel mapping between the sensor and modulator, while using structural gray scale masks instead of binary masks. Moreover, we develop a hybrid convolutional-Transformer deep network for accurate reconstruction of the captured frames. Both simulated and real data experiments demonstrate the superiority of our proposed system over previous approaches, exhibiting significant improvements in terms of spatial resolution and dynamic range.
编码孔径压缩时域成像（CACTI）旨在使用现成的二维传感器单次捕获一系列视频帧。这种方法在降低数据吞吐量要求的同时，有效提高了传感器的帧率。然而，先前的CACTI系统面临诸如空间分辨率有限和动态范围狭窄等挑战，这主要是由次优的光学调制和采样方案导致的。在本信函中，我们提出了一种高效的CACTI系统，该系统通过在传感器和调制器之间采用精确的一对一像素映射来应对这些挑战，同时使用结构灰度掩模而非二进制掩模。此外，我们开发了一种混合卷积-Transformer深度网络，用于精确重建捕获的帧。模拟和实际数据实验均证明了我们提出的系统优于先前的方法，在空间分辨率和动态范围方面展现出显著提升。
