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A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing
Remote Sensing, 2021 The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing.
Shaofei Dai +3 more
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Compressed sensing for active non-line-of-sight imaging.
Optics Express, 2021 Non-line-of-sight (NLOS) imaging techniques have the ability to look around corners, which is of growing interest for diverse applications. We explore compressed sensing in active NLOS imaging and show that compressed sensing can greatly reduce the ...
Juntian Ye +3 more
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Sensors, 2023 High-definition images covering entire large-scene construction sites are increasingly used for monitoring management. However, the transmission of high-definition images is a huge challenge for construction sites with harsh network conditions and scarce
Tuocheng Zeng +4 more
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Hierarchical Compressed Sensing
, 2022 Compressed sensing is a paradigm within signal processing that provides the means for recovering structured signals from linear measurements in a highly efficient manner. Originally devised for the recovery of sparse signals, it has become clear that a similar methodology would also carry over to a wealth of other classes of structured signals. In this
Eisert, Jens +4 more
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Deep Compressed Sensing Generation Model for End-to-End Extreme Observation and Reconstruction
Applied Sciences, 2022 Data transmission and storage are inseparable from compression technology. Compressed sensing directly undersamples and reconstructs data at a much lower sampling frequency than Nyquist, which reduces redundant sampling.
Han Diao, Xiaozhu Lin, Chun Fang
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Message-passing algorithms for compressed sensing [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2009 Compressed sensing aims to undersample certain high-dimensional signals yet accurately reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible when the object to be recovered is sufficiently sparse in a known basis ...
D. Donoho, A. Maleki, A. Montanari
semanticscholar +1 more source
Chinese Journal of Magnetic Resonance, 2018 Compressed sensing is widely used in accelerated magnetic resonance imaging (MRI) to reduce scan time. With compressed sensing, high-quality MR images could be acquired and reconstructed with only a small amount of K space data.
CHAI Qing-huan +2 more
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Research on LFM signal parameter estimation method based on Gabor transform to improve MWC system
AIP Advances, 2023 The “compressed sensing” theory is the foundation for the compressed sampling system’s design. In addition to the sparse representation and observation matrix, more studies in compressed sensing theory focus on signal reconstruction and recovery.
Shuo Meng, Chen Meng, Cheng Wang
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Stochastic Parameterization Using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model
Tellus: Series A, Dynamic Meteorology and Oceanography, 2022 Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing.
A. Mukherjee +3 more
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(Compressed) sensing and sensibility [PDF]
Proceedings of the National Academy of Sciences, 2011 For decades, researchers have built computer models of molecular interactions to predict properties of new molecules (1). These models take the form of potential functions, equations that can be used predict the molecular energy of interaction. Potential functions have very broad applications. Other than ab initio quantum mechanics-based approaches (2),
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