CS-MUVI: Video compressive sensing for spatial-multiplexing cameras

Sankaranarayanan, Aswin C.; Studer, Christoph; Baraniuk, Richard G.

doi:10.1109/iccphot.2012.6215212

Cited by 132 publications

(109 citation statements)

References 17 publications

(39 reference statements)

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“…This can result in long reconstruction times measured in minutes or hours. Recent developments make progress toward compressive video reconstruction [12][13][14][15][16][17][18], with emphasis on developing methods to reduce computational time [19,20]; however, in this paper we concentrate on compressive techniques that do not require computationally expensive reconstruction and are therefore able to provide near video frame rates.…”

Section: Introductionmentioning

confidence: 99%

Single-pixel infrared and visible microscope

Radwell¹,

Mitchell²,

Gibson³

et al. 2014

Optica

378

189

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Microscopy is an essential tool in a huge range of research areas. Until now, microscopy has been largely restricted to imaging in the visible region of the electromagnetic spectrum. Here we present a microscope system that uses single-pixel imaging techniques to produce images simultaneously in the visible and shortwave infrared. We apply our microscope to the inspection of various objects, including a silicon CMOS sensor, highlighting the complementarity of the visible and shortwave infrared wavebands. The system is capable of producing images with resolutions between 32 × 32 and 128 × 128 pixels at corresponding frame rates between 10 and 0.6 Hz. We introduce a compressive technique that does not require postprocessing, resulting in a predicted frame rate increase by a factor 8 from a compressive ratio of 12.5% with only 28% relative error.

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Section: Introductionmentioning

confidence: 99%

Single-pixel infrared and visible microscope

Radwell¹,

Mitchell²,

Gibson³

et al. 2014

Optica

378

189

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show abstract

“…An interesting question is whether both parameter estimation and base signal estimation can be performed simultaneously from compressive measurements (via, say, alternating methods, similar to those developed in [21,22]). We defer this to future work.…”

Section: Base Signal Estimationmentioning

confidence: 99%

LIE operators for compressive sensing

Hegde

Sankaranarayanan

Baraniuk

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We consider the efficient acquisition, parameter estimation, and recovery of signal ensembles that lie on a low-dimensional manifold in a high-dimensional ambient signal space. Our particular focus is on randomized, compressive acquisition of signals from the manifold generated by the transformation of a base signal by operators from a Lie group. Such manifolds factor prominently in a number of applications, including radar and sonar array processing, camera arrays, and video processing. Leveraging the fact that Lie group manifolds admit a convenient analytical characterization, we develop new theory and algorithms for: (1) estimating the Lie operator parameters from compressive measurements, and (2) recovering the base signal from compressive measurements. We validate our approach with several of numerical simulations, including the reconstruction of an affine-transformed video sequence from compressive measurements.

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“…To the best of our knowledge we are the first to propose and justify the use of other interpolation kernels for aggregating measurements. We do note that one algorithm from the literature [21] does present a careful method for grouping the measurements in order to minimize the interpolation error when using a rectangular kernel.…”

Section: A Related Workmentioning

confidence: 99%

“…Finally, one other related algorithm involves a dual-scale reconstruction of a video [21]. First, a sufficient number of low-resolution measurements are collected to permit a low-resolution preview of the video to be obtained using simple least-squares.…”

Section: A Related Workmentioning

confidence: 99%

Multiscale algorithm for reconstructing videos from streaming compressive measurements

Park

Wakin

2013

J. Electron. Imaging

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We propose a multiscale, iterative algorithm for reconstructing video signals from streaming compressive measurements. Our algorithm is based on the observation that, at the imaging sensor, many videos should have limited temporal bandwidth due to the spatial lowpass filtering that is inherent in typical imaging systems. Under modest assumptions about the motion of objects in the scene, this spatial filtering prevents the temporal complexity of the video from being arbitrarily high. Thus, even though streaming measurement systems may measure a video thousands of times per second, we propose an algorithm that only involves reconstructing a much lower rate stream of "anchor frames." Our analysis of the temporal complexity of videos reveals an interesting tradeoff between the spatial resolution of the camera, the speed of any moving objects, and the temporal bandwidth of the video. We leverage this tradeoff in proposing a multiscale reconstruction algorithm that alternates between video reconstruction and motion estimation as it produces finer resolution estimates of the video.

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CS-MUVI: Video compressive sensing for spatial-multiplexing cameras

Cited by 132 publications

References 17 publications

Single-pixel infrared and visible microscope

Single-pixel infrared and visible microscope

LIE operators for compressive sensing

Multiscale algorithm for reconstructing videos from streaming compressive measurements

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