Browsing by Author "Sankaranarayanan, Aswin C."

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    Method and apparatus for compressive acquisition and recovery of dynamic imagery
    (2017-05-16) Baraniuk, Richard G.; Sankaranarayanan, Aswin C.; Rice University; United States Patent and Trademark Office
    A new framework for video compressed sensing models the evolution of the image frames of a video sequence as a linear dynamical system (LDS). This reduces the video recovery problem to first estimating the model parameters of the LDS from compressive measurements, from which the image frames are then reconstructed. We exploit the low-dimensional dynamic parameters (state sequence) and high-dimensional static parameters (observation matrix) of the LDS to devise a novel compressive measurement strategy that measures only the dynamic part of the scene at each instant and accumulates measurements over time to estimate the static parameters. This enables us to lower the compressive measurement rate considerably yet obtain video recovery at a high frame rate that is in fact inversely proportional to the length of the video sequence. This property makes our framework well-suited for high-speed video capture and other applications. We validate our approach with a range of experiments including classification experiments that highlight the purposive nature of our framework.
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    Methods and systems for video compressive sensing for dynamic imaging
    (2017-01-24) Shi, Jianing V.; Sankaranarayanan, Aswin C.; Studer, Christoph Emanuel; Baraniuk, Richard G.; Rice University; United States Patent and Trademark Office
    A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.
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    Methods and systems for video compressive sensing for dynamic imaging
    (2019-01-08) Shi, Jianing V.; Sankaranarayanan, Aswin C.; Studer, Christoph Emanuel; Baraniuk, Richard G.; Rice University; United States Patent and Trademark Office
    A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.
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    Passive and single-viewpoint 3D imaging system
    (2023-06-13) Wu, Yicheng; Boominathan, Vivek; Chen, Huaijin; Sankaranarayanan, Aswin C.; Veeraraghavan, Ashok; William Marsh Rice University; Carnegie Mellon University; United States Patent and Trademark Office
    A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.
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    Passive and single-viewpoint 3D imaging system
    (2024-08-27) Wu, Yicheng; Boominathan, Vivek; Chen, Huaijin; Sankaranarayanan, Aswin C.; Veeraraghavan, Ashok; Rice University; United States Patent and Trademark Office
    A method for a passive single-viewpoint 3D imaging system comprises capturing an image from a camera having one or more phase masks. The method further includes using a reconstruction algorithm, for estimation of a 3D or depth image.
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    Video Compressive Sensing for Spatial Multiplexing Cameras Using Motion-Flow Models
    (SIAM, 2015) Sankaranarayanan, Aswin C.; Xu, Lina; Studer, Christoph; Li, Yun; Kelly, Kevin F.; Baraniuk, Richard G.
    Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded projections using a spatial light modulator (e.g., a digital micromirror device) and a few optical sensors. This approach finds use in imaging applications where full-frame sensors are either too expensive (e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC systems reconstruct static scenes using techniques from compressive sensing (CS). For videos, however, existing acquisition and recovery methods deliver poor quality. In this paper, we propose the CS multiscale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs. Our framework features novel sensing matrices that enable the efficient computation of a low-resolution video preview, while enabling high-resolution video recovery using convex optimization. To further improve the quality of the reconstructed videos, we extract optical-flow estimates from the low-resolution previews and impose them as constraints in the recovery procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of synthetic and real measured SMC video data, and we show that high-quality videos can be recovered at roughly $60\times$ compression.