Abstract

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.

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