Quantization error and dynamic range considerations for compressive imaging systems design

Stern, Adrian; Zeltzer, Yigal; Rivenson, Yair

doi:10.1364/josaa.30.001069

Cited by 10 publications

(7 citation statements)

References 33 publications

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“…6. Effects of finite dynamic range and quantization effects could be significant 23 and will need to be carefully addressed in future studies. Finally, the extensions of this approach to infrared wavelengths must account for stray light and other thermal noise sources.…”

Section: Summary and Future Workmentioning

confidence: 99%

High-resolution imaging using a translating coded aperture

et al. 2017

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Abstract. It is well known that a translating mask can optically encode low-resolution measurements from which higher resolution images can be computationally reconstructed. We experimentally demonstrate that this principle can be used to achieve substantial increase in image resolution compared to the size of the focal plane array (FPA). Specifically, we describe a scalable architecture with a translating mask (also referred to as a coded aperture) that achieves eightfold resolution improvement (or 64∶1 increase in the number of pixels compared to the number of focal plane detector elements). The imaging architecture is described in terms of general design parameters (such as field of view and angular resolution, dimensions of the mask, and the detector and FPA sizes), and some of the underlying design trades are discussed. Experiments conducted with different mask patterns and reconstruction algorithms illustrate how these parameters affect the resolution of the reconstructed image. Initial experimental results also demonstrate that the architecture can directly support task-specific information sensing for detection and tracking, and that moving objects can be reconstructed separately from the stationary background using motion priors.

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Section: Summary and Future Workmentioning

confidence: 99%

High-resolution imaging using a translating coded aperture

et al. 2017

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“…In Ref. 43 a slightly different imaging model is used to better reveal radiometric relations. The general mathematical imaging model in Ref.…”

Section: Hyperspectral Imaging System Comparisonmentioning

confidence: 99%

“…where γ = H Φ is a matrix, representing the sensing process, and γ is a scalar coefficient, accounting for imaging parameters that influence the measurement intensity 43 and is related to the design of the imaging system. In the case of an ideal conventional imaging system, the measurement of each pixel in the object will have an element in the measurement vector g (see Fig.…”

Section: Hyperspectral Imaging System Comparisonmentioning

confidence: 99%

Compressive and classical hyperspectral systems: a fundamental comparison

Shay

August

Stern

2015

Compressive Sensing IV

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Hyperspectral imagery involves capturing and processing a tremendous amount of data, which sets severe system resource requirements. This has motivated the application of compressive sensing for different spectroscopic and spectroscopic imager systems. Several new compressive hyperspectral architectures have been designed to stretch the common limitations of classical systems. However, the application of the compressive sensing framework involves design of system architectures that differ significantly from the conventional ones. Since compressive sensing differs essentially from conventional sensing, it cannot be implemented for hyperspectral imaging by simply modifying one of the components of a conventional hyperspectral system, rather it requires a complete new design. In this work we present a comparison between four compressive hyperspectral architectures to conventional architectures. The compressive hyperspectral sensing compared are: Coded Aperture Snapshot Spectral Imaging (CASSI), Compressive HS Imaging by Separable Spatial And Spectral Operators (CHISSS), (Liquid-crystal Compressive spectral Imager) LiCSI and (Spectral Single-Pixel) SSP systems. Those methods are compared to conventional spatial/spectral scanning hyperspectral such as pushbroom, whiskbroom and color filter techniques. A fundamental comparison between these architectures is presented in terms of optical system volume and radiometric efficiency.

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“…A route to super-resolution has been indicated [18], metamaterial imaging systems aided by CS have been also recently investigated [19]. At the same time, the limitations of CS are now also clear [20][21][22]. Attempts to develop adaptive measurement schemes for use CS have been reported [23].…”

Section: Introductionmentioning

confidence: 99%

Sparse image measurement with an optical single-pixel detector using various schemes of image sampling

Pastuszczak

Kotyński

2015

2015 17th International Conference on Transparent Optical Networks (ICTON)

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We present the theoretical and experimental results of computational ghost imaging using compressive sensing. Our particular interest lies in imaging medical structures in depth of the tissue, compressible in either wavelet or Fourier domain. The measurement is based on a sequence of images from a bucket detector. We establish the optimal measurement and reconstruction basis in order to obtain the best quality of the image reconstruction with possibly few measurements. We consider several schemes of image sampling based on orthonormal basis incoherent either with the Fourier basis or with Daubechies wavelet sets, such as orthonormalised Gaussian random matrices, Hadamard matrices and noiselet matrices. Eventually, the optimal sampling scheme has been introduced into the experimental single-pixel camera set-up for imaging in the visible wavelength range. Keywords: compressive sensing, single-pixel camera, computational imaging.978-1-4673-7880-2/15/$31.00 ©2015 IEEE

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Quantization error and dynamic range considerations for compressive imaging systems design

Cited by 10 publications

References 33 publications

High-resolution imaging using a translating coded aperture

High-resolution imaging using a translating coded aperture

Compressive and classical hyperspectral systems: a fundamental comparison

Sparse image measurement with an optical single-pixel detector using various schemes of image sampling

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