Results 311 to 320 of about 3,086,556 (345)

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
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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.
Gürbüz, A. C., İlbeği, H.
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Dual-Path Attention Network for Compressed Sensing Image Reconstruction

IEEE Transactions on Image Processing, 2020
Although deep neural network methods achieved much success in compressed sensing image reconstruction in recent years, they still have some issues, especially in preserving texture details.
Yubao Sun, Jiwei Chen, Qingshan Liu, Bo Liu, G. Guo   +4 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
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Accelerating SENSE using compressed sensing

Magnetic Resonance in Medicine, 2009
AbstractBoth parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for ...
Dong, Liang, Bo, Liu, Jiunjie, Wang, Leslie, Ying   +3 more
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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

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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Foveated Compressed Sensing

Circuits, Systems, and Signal Processing, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Compressed Motion Sensing

2017
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert, Petra, Stefania, Schnörr, Christoph   +2 more
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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