Results 181 to 190 of about 2,951,538 (214)

Compressed Sensing

, 2012
Machine generated contents note: 1. Introduction to compressed sensing Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar and Gitta Kutyniok; 2. Second generation sparse modeling: structured and collaborative signal analysis Alexey Castrodad, Ignacio ...
Gitta Kutyniok
semanticscholar   +1 more source

Enhanced 3DTV Regularization and Its Applications on HSI Denoising and Compressed Sensing

IEEE Transactions on Image Processing, 2020
The total variation (TV) is a powerful regularization term encoding the local smoothness prior structure underlying images. By combining the TV regularization term with low rank prior, the 3D total variation (3DTV) regularizer has achieved advanced ...
Jiangjun Peng, Qi Xie, Qian Zhao, Yao Wang, L. Yee, Deyu Meng   +5 more
semanticscholar   +1 more source

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.
Qilian Liang, Davis Kirachaiwanich
openaire   +2 more sources

Scalable Convolutional Neural Network for Image Compressed Sensing

Computer Vision and Pattern Recognition, 2019
Recently, deep learning based image Compressed Sensing (CS) methods have been proposed and demonstrated superior reconstruction quality with low computational complexity.
Wuzhen Shi, F. Jiang, Shaohui Liu, Debin Zhao   +3 more
semanticscholar   +1 more source

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   +3 more sources

Deep Convolutional Compressed Sensing for LiDAR Depth Completion

Asian Conference on Computer Vision, 2018
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points. We use techniques from compressed sensing and the recently developed Alternating Direction Neural Networks (ADNNs) to create a deep recurrent auto ...
Nathaniel Chodosh, Chaoyang Wang, S. Lucey   +2 more
semanticscholar   +1 more source

In Situ Compressive Sensing [PDF]

2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2007
Compressive sensing (CS) is a framework that exploits the compressible character of most natural signals, allowing the accurate measurement of an m-dimensional real signal u in terms of n«m real measurements v. The CS measurements may be represented in terms of an n×m matrix that defines the linear relationship between v and u.
Lawrence Carin, Ya Xue, Dehong Liu
openaire   +2 more sources

Compressed sensing radar [PDF]

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
A stylized compressed sensing radar is proposed in which the time-frequency plane is discretized into an N by N grid. Assuming that the number of targets K is small (i.e. KLtN2), then we can transmit a sufficiently ldquoincoherentrdquo pulse and employ the techniques of compressed sensing to reconstruct the target scene.
M.A. Herman, Thomas Strohmer
openaire   +2 more sources

Cosparsity in Compressed Sensing

2015
Analysis l1-recovery is a strategy of acquiring a signal, that is sparse in some transform domain, from incomplete observations. In this chapter we give an overview of the analysis sparsity model and present theoretical conditions that guarantee successful nonuniform and uniform recovery of signals from noisy measurements.
Maryia Kabanava, Holger Rauhut
openaire   +3 more sources

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, Arian Maleki, Urbashi Mitra, Shirin Jalali   +3 more
openaire   +2 more sources