CMOS compressed imaging by Random Convolution

Jacques, Laurent; Vandergheynst, Pierre; Bibet, A.; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf

doi:10.1109/icassp.2009.4959783

Cited by 52 publications

(44 citation statements)

References 11 publications

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“…Similarly, the work in [5] proposes to capture multi-channel images with a single-channel sensor array by applying distinct transformations on every channel followed by a multiplexing step. A particularly promising parallel-acquisition approach for optical CS is called random convolution (RC) [6][7][8][9]. Unlike the aforementioned methods, RC can be implemented using a conventional optical device.…”

Section: Random Convolutionmentioning

confidence: 99%

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Compressed imaging by sparse random convolution

Marcos¹,

Lasser²,

López³

et al. 2016

Opt. Express

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The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC). In contrast with single-pixel CS approaches, RC allows for the parallel capture of visual information on a sensor array as in conventional imaging approaches. Unfortunately, the RC strategy is difficult to implement as is in practical settings due to important contrast-to-noise-ratio (CNR) limitations. In this paper, we introduce a modified RC model circumventing such difficulties by considering measurement matrices involving sparse non-negative entries. We then implement this model based on a slightly modified microscopy setup using incoherent light. Our experiments demonstrate the suitability of this approach for dealing with distinct CS scenarii, including 1-bit CS. Abstract:The theory of compressed sensing (CS) shows that signals can be acquired at sub-Nyquist rates if they are sufficiently sparse or compressible. Since many images bear this property, several acquisition models have been proposed for optical CS. An interesting approach is random convolution (RC). In contrast with single-pixel CS approaches, RC allows for the parallel capture of visual information on a sensor array as in conventional imaging approaches. Unfortunately, the RC strategy is difficult to implement as is in practical settings due to important contrastto-noise-ratio (CNR) limitations. In this paper, we introduce a modified RC model circumventing such difficulties by considering measurement matrices involving sparse non-negative entries. We then implement this model based on a slightly modified microscopy setup using incoherent light. Our experiments demonstrate the suitability of this approach for dealing with distinct CS scenarii, including 1-bit CS.

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Section: Random Convolutionmentioning

confidence: 99%

“…The filters can also be defined in the spatial domain following some random i.i.d. distribution which can be Gaussiantype, Bernoulli-type, or Rademacher-type as in [8] where h[·] 2 { 1, 1}.…”

Section: Sparse Random Convolutionmentioning

confidence: 99%

Compressed imaging by sparse random convolution

Marcos¹,

Lasser²,

López³

et al. 2016

Opt. Express

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“…To have a feasible measurement matrix, we use the Random Convolution strategy explained in the work of J. Romberg [1] for each camera. A different physical realization of this sensing matrix is also discussed in [7]. In short, the method subsamples m random values of the signal x circularly convolved with a random filter.…”

Section: Camera Array Acquisition Schemementioning

confidence: 99%

Light field compressive sensing in camera arrays

Kamal

Golbabaee

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a novel approach to capture light field in camera arrays based on the compressive sensing framework. Light fields are captured by a linear array of cameras with overlapping field of view. In this work, we design a redundant dictionary to exploit crosscameras correlated structures to sparsely represent cameras image. Our main contributions are threefold. First, we exploit the correlations between the set of views by making use of a specially designed redundant dictionary. We show experimentally that the projection of complex scenes onto this dictionary yields very sparse coefficients. Second, we propose an efficient compressive encoding scheme based on the random convolution framework [1]. Finally, we develop a joint sparse recovery algorithm for decoding the compressed measurements and show a marked improvement over independent decoding of CS measurements.

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“…[11] Romberg generalized above random filter, developed a strict theory for this universal CS measurement and derived a bound on the number of samples need to guarantee sparse reconstruction from a strict theoretical perspective. Following the Romberg's theory, L. Jacques et al have constructed the CMOS compressed imaging by using a lot of shift registers in a pseudo-random configuration [12]. Of course, there are other excellent results, for example [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, these CS measurements can not be usually used in practice (at least can not used for the real-time purpose) because of its time-consuming computation and the difficulty of physical realization. To overcome this difficulty, many efforts have been done [8][9][10][11][12]. In the [10], the random filter based on the fixed FIR filter having random taps has been proposed and studied by the numerical simulations, by which one can realize the recovery of sparse signals (in the time/frequency domain, wavelet domain, etc.)…”

Section: Introductionmentioning

confidence: 99%

The Compressed-Sampling Filter (Csf)

Li¹,

Zhang²,

Yin³

et al. 2009

PIER B

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Abstract-The common approaches to sample a signal generally follow the well-known Nyquist-Shannon's theorem: the sampling rate must be at least twice the maximum frequency presented in the signal. A new emerging field, compressed sampling (CS), has made a paradigmatic step to sample a signal with much less measurements than those required by the Nyquist-Shannon's theorem when the unknown signal is sparse or compressible in some frame.We call a compressed-sampling filter (CSF) one for which the function relating the input signal to the output signal is pseudorandom. Motivated by the theory of random convolution proposed by Romberg (for convenience, called the Romberg's theory) and the fact that the signal in complex electromagnetic environment may be spread out due to the rich multi-scattering effect, two CSFs via microwave circuit to enable signal acquisition with sub-Nyquist sampling have been constructed, tested and analyzed. Afterwards, the CSF based on surface acoustic wave (SAW) structure has also been proposed and examined by the numerical simulation. The results have empirically shown that by the proposed architectures the S-sparse ndimensional signal can be exactly reconstructed with O(S log n) realvalued measurements or O(S log(n/S)) complex-valued measurements with overwhelming probability.

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CMOS compressed imaging by Random Convolution

Cited by 52 publications

References 11 publications

Compressed imaging by sparse random convolution

Compressed imaging by sparse random convolution

Light field compressive sensing in camera arrays

The Compressed-Sampling Filter (Csf)

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