&lt;title&gt;A new compressive imaging camera architecture using optical-domain compression&lt;/title&gt;

Takhar, Dharmpal; Laska, Jason N.; Wakin, Michael B.; Duarte, Marco F.; Baron, Dror; Sarvotham, Shriram; Kelly, Kevin F.; Baraniuk, Richard G.

doi:10.1117/12.659602

Cited by 426 publications

(311 citation statements)

References 27 publications

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“…Simply speaking, it is a compressive sensing camera using digital micro-mirror device to generate measurements (for more details see [32]) . In this test, we focused on reconstructing infrared data captured by this single-pixel camera.…”

Section: A Test With Hardware-measured Datamentioning

confidence: 99%

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

Li¹,

Yin²,

Jiang³

et al. 2012

100

106

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Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) with a particular structure. The algorithm effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration. We establish convergence for this algorithm, and apply it to solving problems in image reconstruction with total variation regularization. We present numerical results showing that the resulting solver, called TVAL3, is competitive with, and often outperforms, other state-of-the-art solvers in the field.

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Section: A Test With Hardware-measured Datamentioning

confidence: 99%

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

Li¹,

Yin²,

Jiang³

et al. 2012

100

106

View full text Add to dashboard Cite

show abstract

“…When the endmember spectral signatures W is given, we also propose a preprocessing procedure based on the singular value decomposition of F to substantially reduce the problem sizes, and enable near-real-time processing speeds. The feasibility of the proposed approach has been demonstrated by a test using compressed hyperspectral data collected by hardware similar to the single-pixel camera [7], as shown in Figure 21. Our target image is the color wheel on the left, composed of various intensity levels of three colors: yellow, cyan and magenta.…”

Section: Tv-based Hyperspectral Compressed Sensing and Unmixingmentioning

confidence: 99%

Compressive Hyperspectral Imaging and Anomaly Detection

Thiyanarantnam¹,

Osher²,

Chen³

et al. 2013

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“…Compressive Sensing Compressive sensing [Candes et al, 2006;Donoho, 2006] is a nascent field of applied mathematics with a variety of successful applications including imaging [Takhar et al, 2006;Willett et al, 2007], medical visualization [Lustig et al, 2007], and face recognition [Wright et al, 2009]. Recently, compressive sensing has also been widely used to solve many computer vision and computer graphics problems, such as high speed imaging [Veeraraghavan et al, 2011;Sankaranarayanan et al, 2010;Hitomi et al, 2011], image restoration and denoising [Mairal et al, 2009;Elad and Aharon, 2006;Protter and Elad, 2009], and light transport measurement [Peers et al, 2009;Sen and Darabi, 2009].…”

Section: Related Workmentioning

confidence: 99%

Compressive Structured Light for Recovering Inhomogeneous Participating Media

Nayar

Grinspun

et al. 2008

Lecture Notes in Computer Science

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Abstract-We propose a new method named compressive structured light for recovering inhomogeneous participating media. Whereas conventional structured light methods emit coded light patterns onto the surface of an opaque object to establish correspondence for triangulation, compressive structured light projects patterns into a volume of participating medium to produce images which are integral measurements of the volume density along the line of sight. For a typical participating medium encountered in the real world, the integral nature of the acquired images enables the use of compressive sensing techniques that can recover the entire volume density from only a few measurements. This makes the acquisition process more efficient and enables reconstruction of dynamic volumetric phenomena. Moreover, our method requires the projection of multiplexed coded illumination, which has the added advantage of increasing the signal-to-noise ratio of the acquisition. Finally, we propose an iterative algorithm to correct for the attenuation of the participating medium during the reconstruction process. We show the effectiveness of our method with simulations as well as experiments on the volumetric recovery of multiple translucent layers, 3D point clouds etched in glass, and the dynamic process of milk drops dissolving in water.

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<title>A new compressive imaging camera architecture using optical-domain compression</title>

Cited by 426 publications

References 27 publications

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

Compressive Hyperspectral Imaging and Anomaly Detection

Compressive Structured Light for Recovering Inhomogeneous Participating Media

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