Results 11 to 20 of about 42,861 (285)
Compression-Based Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2017 Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their under-determined set of linear projections.
Farideh E. Rezagah +3 more
openaire +2 more sources
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
openaire +2 more sources
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
doaj +1 more source
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
doaj +1 more source
Sensors, 2020 With the widespread application of wireless sensor networks, large-scale systems with high sampling rates are becoming more and more common. The amount of original data generated by the wireless sensor network is very large, and transmitting all the ...
Youtian Qie, Chuangbo Hao, Ping Song
doaj +1 more source
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
doaj +1 more source
Surface Measurement Using Compressed Wavefront Sensing
Photonic Sensors, 2018 Compressed sensing leverages the sparsity of signals to reduce the amount of measurements required for its reconstruction. The Shack-Hartmann wavefront sensor meanwhile is a flexible sensor where its sensitivity and dynamic range can be adjusted based on
Eddy Mun Tik Chow +3 more
doaj +1 more source
Spatial-Spectral Joint Compressed Sensing for Hyperspectral Images
IEEE Access, 2020 Compressed sensing is one of the key technologies to reduce the volume of hyperspectral image for real-time storage and transmission. Reconstruction based on spectral unmixing show tremendous potential in hyperspectral compressed sensing compared with ...
Zhongliang Wang +5 more
doaj +1 more source
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
doaj +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
doaj +1 more source