CMOS low data rate imaging method based on compressed sensing

Xiao, Lan; Liu, Kun; Han, Dong

doi:10.1016/j.optlastec.2011.12.033

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…CMOS-based compressive sensing approaches have recently emerged for optical imaging [11,21,22]. In addition to the aforementioned optics-based designs, it is possible to combine these CMOS-based approaches with standard pushbroom or framing designs to reduce the number of measurements taken with respect to the number of voxels.…”

Section: Sparse Models and Hyperspectral Imagersmentioning

confidence: 99%

“…The resulting designs, however, do not change the latency, spectral, or spatial resolution of the resulting compressive camera (when compared to a standard sensor-array camera of the same size and count). Existing implementations of compressive optical sensor arrays perform the computation of the required projections using metal-oxide-semiconductor electronics and are based on random convolution [22], separable transformations [21], block-based transforms [22], structured incoherent transforms like noiselets [11], and randomized integration via Sigma-Delta ADCs [23]. The resulting measurement matrices are expressed in terms of a Kronecker product I ⊗ A CM OS , where A CM OS denotes the measurement operator implemented by the CMOS design and the Kronecker product represents the replication of the measurement process among the snapshots required by the particular camera design (e.g., across spectral bands for a staring camera or across shifts in a spatial dimension for a pushbroom camera).…”

Section: Sparse Models and Hyperspectral Imagersmentioning

confidence: 99%

See 1 more Smart Citation

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Willett

Duarte

Davenport

et al. 2014

IEEE Signal Process. Mag.

180

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Section: Sparse Models and Hyperspectral Imagersmentioning

confidence: 99%

Section: Sparse Models and Hyperspectral Imagersmentioning

confidence: 99%

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Willett

Duarte

Davenport

et al. 2014

IEEE Signal Process. Mag.

180

View full text Add to dashboard Cite

“…In [64,65], passive pixel sensors (PPS) [66] were used to design a separable-transform image sensor which is also capable of implementing the CS encoding. Instead of sensing the pixel values for the image, the imager projects the image on a specific basis and produces the projection coefficients.…”

Section: Hardware Implementationmentioning

confidence: 99%

Compressive Sensing Image Sensors-Hardware Implementation

Dadkhah

Deen

Shirani

2013

Sensors

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The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal–oxide–semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.

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“…The CS-based remote sensing includes two stages: onboard encoding imaging and offline decoding recovery. Previous works in [1,15] studies the onboard encoding imaging. In this paper, we investigate a linearized Bregman error iteration algorithm for l1-Regularized minimization with applications to the Infrared Video offline decoding recovery.…”

Section: Introductionmentioning

confidence: 99%

“…The models(10) and(11) can easily improve speed for video reconstruction, and the linearized Bregman iteration method converge in much shorter times than the original Bregman iteration method is prove in[15…”

mentioning

confidence: 99%

CS-Based infrared remote sensing video reconstruction with a linearized Bregman error iteration algorithm for l1-regularized minimization

Liu

Wang

et al. 2014

SPIE Proceedings

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Abstract：Compressed sensing (CS) breaks Shannon/Nyquist sampling theorem bottleneck. It captures and represents signals at a sampling rate significantly below the Nyquist rate, and then original signals can be accurately or high precisely recovered by solving sparse optimization problems based on signal sparsity or compressibility. CS has a good application prospect in remote sensing imagery, especially in Infrared Remote Sensing Video. The CS-based remote sensing includes two stages: onboard encoding imaging and offline decoding recovery. Video offline decoding recovery is one of the core questions in CS-Based Infrared Remote Sensing Video systems. In this paper, firstly, we introduce a coupled optimization models which is composed of a single model and an error model for video offline decoding recovery. This paper shows that the coupled models can easily improve speed and accuracy to recover video of Infrared Remote Sensing. Secondly, we review the Bregman method and linearized Bregman method. Furthermore, a linearized Bregman error iteration algorithm is proposed for solving the coupled models, thus lead to better convergence rates and error performances. In numerical experiments, we compare the convergence rates of the original Bregman method and the linearized Bregman method from the first frame picture, and test the performance of the linearized Bregman method for video recovery with the single model and coupled models. Numerical experiments demonstrate the effectiveness of the proposed algorithm. The comparison with the single model is included.Compressed Sensing ， Infrared Remote Sensing Video, Linearized Bregman Algorithm, l1-Regularized Minimization

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CMOS low data rate imaging method based on compressed sensing

Cited by 14 publications

References 18 publications

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Sparsity and Structure in Hyperspectral Imaging : Sensing, Reconstruction, and Target Detection

Compressive Sensing Image Sensors-Hardware Implementation

CS-Based infrared remote sensing video reconstruction with a linearized Bregman error iteration algorithm for l1-regularized minimization

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