Compressive holographic video

Abstract

Compressed sensing has been discussed separately in spatial and temporal domains. Compressive holography has been introduced as a method that allows 3D tomographic reconstruction at different depths from a single 2D image. Coded exposure is a temporal compressed sensing method for high speed video acquisition. In this work, we combine compressive holography and coded exposure techniques and extend the discussion to 4D reconstruction in space and time from one coded captured image. In our prototype, digital in-line holography was used for imaging macroscopic, fast moving objects. The pixel-wise temporal modulation was implemented by a digital micromirror device. In this paper we demonstrate 10× temporal super resolution with multiple depths recovery from a single image. Two examples are presented for the purpose of recording subtle vibrations and tracking small particles within 5 ms.

Deep fully-connected networks for video compressive sensing

“…The additional components operate at higher frame rates than the camera's native temporal resolution giving rise to low frame rate multiplexed measurements which can later be decoded to extract the unknown observed high speed video sequence. Despite its use for high speed motion capture [24], video CS also has applications to coherent imaging (e.g., holography) for tracking high-speed events [41] (e.g., particle tracking, observing moving biological samples). The benefits of video CS are even more pronounced for non-visible light applications where high speed cameras are rarely available or prohibitively expensive (e.g., millimeter-wave imaging, infrared imaging) [2,4].…”

Event-Driven Video Frame Synthesis

“…Our paper investigates the temporal upsampling problem. While previous approaches investigate in the framework of compressive sensing [1,14,17,26,35,38,41], we formulate our work as fusing event streams with intensity images to obtain a temporally dense video. Compared to existing literature [36] which integrates event counts per pixel across time, our differentiable model utilizes "tanh" functions as event activation units and imposes sparsity constraints on both spatial and temporal domain.…”

Computational multifocal microscopy

“…This limitation prevents broader applications such as in vivo imaging as the motion of the objects during capture process is hard to circumvent and thus, deteriorates the image quality [19,20]. Recent works have proposed to reduce the measuring requirement by computationally exploiting spatial-temporal redundancy of the scenes [21,22]. Here, we review two types of computational microscopy that enable single-shot 3D fluorescence imaging.…”