Lensless imaging by compressive sensing

Huang, Gang; Jiang, Hong; Matthews, Kim; Wilford, Paul A.

doi:10.1109/icip.2013.6738433

Cited by 150 publications

(156 citation statements)

References 13 publications

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“…For example, compressive sensing or ghost imaging techniques based on random mask projections (Wakin et al, 2006; Tian et al, 2011; Studer et al, 2012; Sun et al, 2013a) might allow a smaller number of photodetectors to be used. In an illustrative case, an imaging system may be constructed simply from a single photodetector and a transmissive LCD screen presenting a series of random binary mask patterns (Huang et al, 2013), where the number of required mask patterns is much smaller than the number of image pixels due to a compressive reconstruction.…”

Section: Evaluation Of Modalitiesmentioning

confidence: 99%

Physical principles for scalable neural recording

Marblestone

Zamft

Maguire

et al. 2013

Front. Comput. Neurosci.

229

223

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Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.

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Section: Evaluation Of Modalitiesmentioning

confidence: 99%

Physical principles for scalable neural recording

Marblestone

Zamft

Maguire

et al. 2013

Front. Comput. Neurosci.

229

223

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“…In recent years, coded aperture-based systems using compressive sensing principles [5,10,2] have been studied for image super-resolution [17], spectral imaging [20], and video capture [16]. Maskbased lens-free designs have also been proposed for flexible field-of-view selection in [21], compressive single-pixel imaging using a transmissive LCD panel [15], and for separable coded masks [8].…”

Section: Related Workmentioning

confidence: 99%

“…As a consequence, the light throughput of our designs are many orders of magnitude larger as compared to previous designs. Furthermore, the lensless cameras proposed in [15,8] use programmable spatial light modulators (SLM) and capture multiple images while changing the mask patterns. In contrast, we use a static mask in our design, which can potentially be fixed on the sensor during fabrication or the assembly process.…”

Section: Related Workmentioning

confidence: 99%

FlatCam: Replacing Lenses with Masks and Computation

Asif

Ayremlou

Veeraraghavan

et al. 2015

2015 IEEE International Conference on Computer Vision Workshop (ICCVW)

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We present a thin form-factor lensless camera, FlatCam, that consists of a coded mask placed on top of a bare, conventional sensor array. FlatCam is an instance of a coded aperture imaging system in which each pixel records a linear combination of light from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. In contrast with vast majority of coded aperture systems, we place the coded mask extremely close to the image sensor that can enable a thin system. We use a separable mask to ensure that both calibration and image reconstruction are scalable in terms of memory requirements and computational complexity. We demonstrate the potential of our design using a prototype camera built using commercially available sensor and mask.

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“…Some of these implementations are able to capture scenes in compressed form based on sequential single-pixel acquisitions. This is the case of the one-pixel camera from Rice University [2] and of the Bell Lab's lenseless camera [3]. By contrast, the MIT random lens camera [4] uses a sensor array as in conventional settings.…”

Section: Random Convolutionmentioning

confidence: 99%

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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Lensless imaging by compressive sensing

Cited by 150 publications

References 13 publications

Physical principles for scalable neural recording

Physical principles for scalable neural recording

FlatCam: Replacing Lenses with Masks and Computation

Compressed imaging by sparse random convolution

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