Results 41 to 50 of about 3,276,837 (200)
AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing [PDF]
IEEE Transactions on Image Processing, 2020 Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding ...
Zhonghao Zhang +4 more
semanticscholar +1 more source
"Compressed" Compressed Sensing
, 2010 The field of compressed sensing has shown that a sparse but otherwise arbitrary vector can be recovered exactly from a small number of randomly constructed linear projections (or samples). The question addressed in this paper is whether an even smaller number of samples is sufficient when there exists prior knowledge about the distribution of the ...
Reeves, Galen, Gastpar, Michael
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A New Method for EEG Compressive Sensing
Advances in Electrical and Computer Engineering, 2012 The paper investigates the possibility of using compressive sensing techniques for the acquisition and reconstruction of EEG signals containing the evoked potential P300. A method of EEG compressive sensing based on the physiological correlation of EEG
FIRA, M., GORAS, L.
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Sensors, 2014 Compressive Sensing Imaging (CSI) is a new framework for image acquisition, which enables the simultaneous acquisition and compression of a scene. Since the characteristics of Compressive Sensing (CS) acquisition are very different from traditional image
Xiangwei Li +4 more
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Adaptive Compressed Sensing for Support Recovery of Structured Sparse Sets [PDF]
, 2016 This paper investigates the problem of recovering the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately recovering such sets ...
Castro, Rui M., Tánczos, Ervin
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Efficient Compressive Sensing with Deterministic Guarantees Using Expander Graphs [PDF]
, 2007 Compressive sensing is an emerging technology which can recover a sparse signal vector of dimension n via a much smaller number of measurements than n.
Hassibi, Babak, Xu, Weiyu
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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
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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
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Model-Based Compressive Sensing [PDF]
IEEE Transactions on Information Theory, 2008 Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis.
Richard Baraniuk +3 more
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Optimization-Inspired Compact Deep Compressive Sensing
IEEE Journal on Selected Topics in Signal Processing, 2020 In order to improve CS performance of natural images, in this paper, we propose a novel framework to design an OPtimization-INspired Explicable deep Network, dubbed OPINE-Net, for adaptive sampling and recovery.
Jian Zhang, Chen Zhao, Wen Gao
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