Low-complexity video compression and compressive sensing

Asif, M. Salman; Fernandes, Felix C. A.; Romberg, Justin

doi:10.1109/acssc.2013.6810345

Cited by 28 publications

(29 citation statements)

References 12 publications

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“…1, the wave atom dictionary is applied to represent the ultrasound RF signals in the sparse domain. But in recovery, instead of using the traditional l1-norm or Basis Pursuit, the optimization problem used here is based on regularized-l1 [24] which can be written as…”

Section: Wave Atom Based Compressive Sensingmentioning

confidence: 99%

Wave atom based Compressive Sensing and adaptive beamforming in ultrasound imaging

Foroozan

Sadeghi

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The paper investigates combining Compressive Sensing (CS) with the robust Capon beamformer (RCB) for the purpose of medical ultrasound image formation with a much reduced number of samples compared to those used in current state-of-art ultrasound. The proposed CS algorithm uses wave atom dictionary as a low dimension projection, a Bernouli random matrix as a sensing matrix and a regularized-l1 optimization technique for recovery. The reconstructed signals are then pre-processed before using the RCB technique augmented with spatial smoothing and diagonal loading. This approach is demonstrated through simulations, wire phantom and in vivo cardiac data with a reduction of up to 1/8 in the processed data rate and ultrasound images of similar perceived quality.

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Section: Wave Atom Based Compressive Sensingmentioning

confidence: 99%

Wave atom based Compressive Sensing and adaptive beamforming in ultrasound imaging

Foroozan

Sadeghi

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In the SPC, light from the scene is focused onto a programmable DMD, which directs light from only a subset of activated micro-mirrors onto the photodetector. By changing the micro-mirror configurations, we can obtain linear measurements corresponding to the sensing model in (1). Several multi-pixel extensions to the SPC have been proposed recently, with the goal of increasing the measurement rate [16,17,20,36].…”

Section: Related Workmentioning

confidence: 99%

“…SMCs for video CS also make use of a diverse set of signal models and constraints including 3D wavelets [35], multi-scale wavelet lifting [23], optical flow-based reconstructions [27,1], block-based models [9], sparse frame-toframe residuals [32,5], linear dynamical systems [31,28], and combinations of low-rank and sparse matrices [37]. One characteristic of all these algorithms is that reconstruction performance improves with increasing number of measurements.…”

Section: Related Workmentioning

confidence: 99%

FPA-CS: Focal plane array-based compressive imaging in short-wave infrared

Chen

Asif

Sankaranarayanan

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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Cameras for imaging in short and mid-wave infrared spectra are significantly more expensive than their counterparts in visible imaging. As a result, high-resolution imaging in those spectrum remains beyond the reach of most consumers. Over the last decade, compressive sensing (CS) has emerged as a potential means to realize inexpensive shortwave infrared cameras. One approach for doing this is the single-pixel camera (SPC) where a single detector acquires coded measurements of a high-resolution image. A computational reconstruction algorithm is then used to recover the image from these coded measurements. Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions.We present a focal plane array-based compressive sensing (FPA-CS) architecture that achieves high spatial and temporal resolutions. The idea is to use an array of SPCs that sense in parallel to increase the measurement rate, and consequently, the achievable spatio-temporal resolution of the camera. We develop a proof-of-concept prototype in the short-wave infrared using a sensor with 64× 64 pixels; the prototype provides a 4096× increase in the measurement rate compared to the SPC and achieves a megapixel resolution at video rate using CS techniques.

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“…First, we review the recovery method we used based on regularized-`1 [26], and then the Delay-and-Sum frequency domain beamforming to reconstruct the image.…”

Section: Reconstruction Techniques In 1d and 2dmentioning

confidence: 99%

“…But given the random structure of the sensing matrix , we expect that this will not have a significant effect with experimentally acquired ultrasound data and the performance in practical situations. In recovery, instead of using the traditional 1-norm or basis pursuit, the optimization problem used is based on regularized-`1 [16,26] which can be written as…”

Section: Fourier Domain Signal Reconstructionmentioning

confidence: 99%

Structurally random Fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging

Foroozan¹,

Yousefi

Sadeghi

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Advances in ultrasound technology have fueled the emergence of Point-Of-Care Ultrasound (PoCU) imaging, including improved ease-of-use, superior image quality, and lower cost ultrasound. One of the approaches that can make the adoption of PoCU universal is to make the data acquisition module as simple as a "stethoscope" while further processing and image construction can be done using cloud-based processors. Toward this goal, we use Structurally Random Matrices (SRM) for compressive sensing of ultrasound data, Fourier sparsifying matrix for recovery in 1D, and frequency domain approach for 2D ultrasound image reconstruction. This approach is demonstrated through wire phantom and in vivo carotid arteries data from ultrasound system using 25%, 12.5%, and 6.25% of the full data rate and ultrasound images of similar perceived quality quantified by Structural Similarity Index Metric (SSIM).

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Low-complexity video compression and compressive sensing

Cited by 28 publications

References 12 publications

Wave atom based Compressive Sensing and adaptive beamforming in ultrasound imaging

Wave atom based Compressive Sensing and adaptive beamforming in ultrasound imaging

FPA-CS: Focal plane array-based compressive imaging in short-wave infrared

Structurally random Fourier domain compressive sampling and frequency domain beamforming for ultrasound imaging

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