Results 61 to 70 of about 2,932,031 (355)
Compressively Sensed Image Recognition [PDF]
2018 7th European Workshop on Visual Information Processing (EUVIP), 2018 6 pages, submitted/accepted, EUVIP ...
Degerli A. +4 more
openaire +6 more sources
Deep Generative Adversarial Neural Networks for Compressive Sensing MRI
IEEE Transactions on Medical Imaging, 2019 Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed.
M. Mardani +6 more
semanticscholar +1 more source
Quasi-linear Compressed Sensing [PDF]
Multiscale Modeling & Simulation, 2014 Inspired by significant real-life applications, in particular, sparse phase retrieval and sparse pulsation frequency detection in Asteroseismology, we investigate a general framework for compressed sensing, where the measurements are quasi-linear.
Martin Ehler +2 more
openaire +2 more sources
Adaptive compressive sensing of images using error between blocks
International Journal of Distributed Sensor Networks, 2018 Block compressive sensing of image results in blocking artifacts and blurs when reconstructing images. To solve this problem, we propose an adaptive block compressive sensing framework using error between blocks.
Ran Li, Xiaomeng Duan, Yongfeng Lv
doaj +1 more source
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 Systematic Review of Compressive Sensing: Concepts, Implementations and Applications
IEEE Access, 2018 Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain.
M. Rani, S. B. Dhok, R. Deshmukh
semanticscholar +1 more source
Compressive sensing: Performance comparison of sparse recovery algorithms [PDF]
Computing and Communication Workshop and Conference, 2018 Spectrum sensing is an important process in cognitive radio. Spectrum sensing techniques suffer from high processing time, hardware cost, and computational complexity.
Youness Arjoune +3 more
semanticscholar +1 more source
Compressive Sensing Image Sensors-Hardware Implementation
Sensors, 2013 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 ...
Shahram Shirani +2 more
doaj +1 more source
A compressive data gathering method based on El Gamal cryptography
Dianxin kexue, 2019 As a hot researching area,compressive sensing has advantages in signal process,image processing and data gathering and analysis.How to effectively and safely apply compressive sensing technology to collect data in wireless sensor networks is studied ...
Xiaohan YU +3 more
doaj +2 more sources
, 2013 In compressive sensing, the basis pursuit algorithm aims to find the sparsest solution to an underdetermined linear equation system. In this paper, we generalize basis pursuit to finding the sparsest solution to higher order nonlinear systems of ...
Dong, Roy +3 more
core +1 more source