Results 231 to 240 of about 136,882 (282)

Comparative evaluation of four reconstruction techniques for prostate T2-weighted MRI: Sensitivity encoding, compressed sensing, deep learning, and super-resolution. [PDF]

Eur J Radiol Open
Nishioka N, Fujima N, Tsuneta S, Kato D, Kamiishi T, Yoshikawa M, Kimura R, Sakamoto K, Matsumoto R, Abe T, Kwon J, Yoneyama M, Kudo K.   +12 more
europepmc   +1 more source

Retrospective temporal resolution interpolation alters myocardial strain quantification on compressed sensing cine CMR. [PDF]

Int J Cardiovasc Imaging
Grob L, Schwerzmann Y, Kaiser D, Jung B, Schweizer T, Huettenmoser SP, Dozio S, Huber AT, Boscolo Berto M, Gräni C, Guensch DP, Fischer K.   +11 more
europepmc   +1 more source

Optimization of the acceleration of compressed sensing in whole-heart contrast-free coronary magnetic resonance angiography. [PDF]

J Cardiovasc Magn Reson
Wang W, Zhang L, Su G, Xiong F, Wu Y, Yu K, Yi Q, Sun P.   +7 more
europepmc   +1 more source

Artificial Intelligence-Assisted Compressed Sensing Technique Accelerates Magnetic Resonance Imaging Simulation for Head and Neck Cancer Radiation Therapy. [PDF]

Adv Radiat Oncol
Zhou SH, Lin MS, Luo Y, He HQ, Wang SJ, Shang LT, Dong TY, Fan WJ, Chi F.   +8 more
europepmc   +1 more source

Evaluation of Real-Time Cardiovascular Flow MRI Using Compressed Sensing in a Phantom and in Patients With Valvular Disease or Arrhythmia. [PDF]

J Magn Reson Imaging
Lala T, Christierson L, Frieberg P, Giese D, Kellman P, Hakacova N, Sjöberg P, Ostenfeld E, Töger J.   +8 more
europepmc   +1 more source

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Compressive sensing: To compress or not to compress

2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011
In this paper, we consider the compressive sensing scheme from the information theory point of view and derive the lower bound of the probability of error for CS when length N of the information vector is large. The result has been shown that, for an i.i.d.
Davis Kirachaiwanich, Qilian Liang
openaire   +1 more source

Compressed sensing of compressible signals

2017 IEEE International Symposium on Information Theory (ISIT), 2017
A novel low-complexity robust-to-noise iterative algorithm named compression-based gradient descent (C-GD) algorithm is proposed. C-GD is a generic compressed sensing recovery algorithm, that at its core, employs compression codes, such as JPEG2000 and MPEG4.
Sajjad Beygi, Shirin Jalali, Arian Maleki, Urbashi Mitra   +3 more
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Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications

Applied Sciences (Switzerland), 2020
With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication.
Lixiang Li, Haipeng Peng, Jürgen Kurths   +2 more
exaly   +3 more sources

Demosaicking with compressive sensing

2012 20th Signal Processing and Communications Applications Conference (SIU), 2012
Sparse signals can be recovered with less number of measurements compared to standard methods using Compressive Sensing (CS) theory. In digital cameras, color filter arrays (CFA) are used to sample each color band with less measurements than the normal. The color images are reconstructed using interpolation of measured pixel values.
Handan Ilbegi, Ali Cafer Gürbüz
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Kronecker Compressive Sensing

IEEE Transactions on Image Processing, 2012
Compressive sensing (CS) is an emerging approach for the acquisition of signals having a sparse or compressible representation in some basis. While the CS literature has mostly focused on problems involving 1-D signals and 2-D images, many important applications involve multidimensional signals; the construction of sparsifying bases and measurement ...
Marco F. Duarte, Richard G. Baraniuk
openaire   +2 more sources