  Lensless imagers based on diffusers or encoding masks enable high-dimensional
imaging from a single shot measurement and have been applied in various
applications. However, to further extract image information such as edge
detection, conventional post-processing filtering operations are needed after
the reconstruction of the original object images in the diffuser imaging
systems. Here, we present the concept of a temporal compressive edge detection
method based on a lensless diffuser camera, which can directly recover a time
sequence of edge images of a moving object from a single-shot measurement,
without further post-processing steps. Our approach provides higher image
quality during edge detection, compared with the conventional post-processing
method. We demonstrate the effectiveness of this approach by both numerical
simulation and experiments. The proof-of-concept approach can be further
developed with other image post-process operations or versatile computer vision
assignments toward task-oriented intelligent lensless imaging systems.
