Compressed Sensing: Theory and Applications

Wang, Hanbo

doi:10.1088/1742-6596/2419/1/012042

Cited by 6 publications

(3 citation statements)

References 22 publications

(19 reference statements)

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“…CS is a signal processing concept proposed by David Donoho and others [15,16,19,75], which is a technique for finding sparse solutions to underdetermined linear systems. CS is a signal-processing technique that can achieve signals with much lower sampling rates than those required by traditional sampling theory.…”

Section: Compressed Sensingmentioning

confidence: 99%

“…Choosing an appropriate sparse dictionary can ensure that the signal representation coefficients are sparse enough, thereby reducing the number of compressed sensing measurements related to non-zero coefficients and reconstructing the signal with high probability. Both sparse transform bases and sparse dictionaries satisfy the mathematical model of sparse representation in CS [16]:…”

Section: Sparse Representationmentioning

confidence: 99%

“…The emergence of Compressed Sensing (CS) has provided a new direction for compression technology. CS is capable of retaining essential data, discarding significant amounts of redundant data, and reconstructing the original data with redundancy using a small amount of essential data through known mapping relationships [15][16][17][18]. CS has been widely applied in the field of signal processing since its introduction [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressed Sensing for Biomedical Photoacoustic Imaging: A Review

Wang,

Chen,

Zhao

et al. 2024

Sensors

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Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.

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Section: Compressed Sensingmentioning

confidence: 99%

Section: Sparse Representationmentioning

confidence: 99%