Multichannel sampling schemes for optical imaging systems

Portnoy, Andrew D.; Pitsianis, Nikos P.; Sun, Xiaobai; Brady, David J.

doi:10.1364/ao.47.000b76

Cited by 14 publications

(9 citation statements)

References 16 publications

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“…The detector is a Lumenera CCD with 5:6 μm pixels. A 4 × 4 microlens array was used to obtain sixteen low-resolution images in each frame [14]. The camera, an image obtained from this camera, and the reconstructed image are shown in Figs.…”

Section: B Experiments and Resultsmentioning

confidence: 99%

“…Here we explore the opportunities and challenges in the design of compressive video sensors and corresponding algorithms. In particular, we explore the possibilities of obtaining compression of video using multichannel (TOMBO) cameras that have been described previously [12][13][14]. A mathematical description of a multichannel imaging system is given below to help understand the process of image capture from these cameras.…”

Section: Introductionmentioning

confidence: 99%

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Compressive video sensors using multichannel imagers

2010

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We explore the possibilities of obtaining compression in video through modified sampling strategies using multichannel imaging systems. The redundancies in video streams are exploited through compressive sampling schemes to achieve low power and low complexity video sensors. The sampling strategies as well as the associated reconstruction algorithms are discussed. These compressive sampling schemes could be implemented in the focal plane readout hardware resulting in drastic reduction in data bandwidth and computational complexity.

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Section: B Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressive video sensors using multichannel imagers

2010

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“…It is important to note, however, that the actual object information contained in the raw petapixel photon flux is always much less than the pixel limit in natural fields. Noting recent progress in using compressive sampling to reduce bandwidth and pixel sampling rates [9][10][11][12] in addition to coding spectral [13] and focal [14] degrees of freedom, one may reasonably expect physical layer compression to reduce the camera data load by 4-5 orders of magnitude. In combination with parallel read-out enabled by multiscale design, this suggests that full petapixel data cube sensitivity may eventually be achievable.…”

Section: Information Capacity and Multiscale Designmentioning

confidence: 99%

Petapixel photography and the limits of camera information capacity

Brady

Marks

Feller

et al. 2013

Computational Imaging XI

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The monochromatic single frame pixel count of a camera is limited by diffraction to the space-bandwidth product, roughly the aperture area divided by the square of the wavelength. We have recently shown that it is possible to approach this limit using multiscale lenses for cameras with space bandwidth product between 1 and 100 gigapixels. When color, polarization, coherence and time are included in the image data cube, camera information capacity may exceed 1 petapixel/second. This talk reviews progress in the construction of DARPA AWARE gigapixel cameras and describes compressive measurement strategies that may be used in combination with multiscale systems to push camera capacity to near physical limits.

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“…Many researchers have studied how to improve optical imaging resolution based on the above two points. Andrew D. Portnoy et al [5,6] use focal plane coding to produce non-degenerate data between multiple apertures, and then sub-aperture data is integrated to form a single high resolution image. A compressive imaging system using multiplex and multi-channel measurements with static focal plane coding is described in Ref.…”

Section: Introductionmentioning

confidence: 99%

Quantitative characterization of super-resolution infrared imaging based on time-varying focal plane coding

Wang

Yuan²,

Zhang

et al. 2014

J. Eur. Opt. Soc.-Rapid Publ.

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High resolution infrared image has been the goal of an infrared imaging system. In this paper, a super-resolution infrared imaging method using time-varying coded mask is proposed based on focal plane coding and compressed sensing theory. The basic idea of this method is to set a coded mask on the focal plane of the optical system, and the same scene could be sampled many times repeatedly by using time-varying control coding strategy, the super-resolution image is further reconstructed by sparse optimization algorithm. The results of simulation are quantitatively evaluated by introducing the Peak Signal-to-Noise Ratio (PSNR) and Modulation Transfer Function (MTF), which illustrate that the effect of compressed measurement coefficient r and coded mask resolution m on the reconstructed image quality. Research results show that the proposed method will promote infrared imaging quality effectively, which will be helpful for the practical design of new type of high resolution infrared imaging systems.

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Multichannel sampling schemes for optical imaging systems

Cited by 14 publications

References 16 publications

Compressive video sensors using multichannel imagers

Compressive video sensors using multichannel imagers

Petapixel photography and the limits of camera information capacity

Quantitative characterization of super-resolution infrared imaging based on time-varying focal plane coding

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