Results 21 to 30 of about 13,996 (160)
A Light Field Sparse and Reconstruction Framework for Improving Rendering Quality
IEEE Access, 2020 We present a sparse reconstruction light field (SRLF) framework using compressed sensing theory. Light field signals have sparsity in some specific phenomena, such as nonocclusions, smooth surfaces and texture uniformity.
Hong Zhang +6 more
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A Novel Iterative Thresholding Algorithm Based on Plug-and-Play Priors for Compressive Sampling
Future Internet, 2017 We propose a novel fast iterative thresholding algorithm for image compressive sampling (CS) recovery using three existing denoisers—i.e., TV (total variation), wavelet, and BM3D (block-matching and 3D filtering) denoisers.
Lingjun Liu, Zhonghua Xie, Cui Yang
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Sensors, 2023 This paper considers the problem of estimating the parameters of a frequency-hopping signal under non-cooperative conditions. To make the estimation of different parameters independently of each other, a compressed domain frequency-hopping signal ...
Weipeng Zhu +3 more
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Entropy, 2015 Assuming sparsity or compressibility of the underlying signals, compressed sensing or compressive sampling (CS) exploits the informational efficiency of under-sampled measurements for increased efficiency yet acceptable accuracy in information gathering,
Jingxiong Zhang +3 more
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Broadband Microwave Spectrum Sensing Based on Photonic RF Channelization and Compressive Sampling
IEEE Photonics Journal, 2020 A novel approach to realize broadband microwave spectrum sensing based on photonic RF channelization and compressive sampling (CS) is proposed. The photonic RF channelization system is used to slice the input broadband signal into multiple sub-channel ...
Bo Yang +5 more
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Dynamic range analysis of one-bit compressive sampling with time-varying thresholds
The Journal of Engineering, 2019 From the point of view of statistical signal processing, the dynamic range for one-bit quantisers with time-varying thresholds is studied. Maximum tolerable amplitudes, minimum detectable amplitudes and dynamic ranges of this one-bit sampling approach ...
Xiaofeng Zhu, Liang Huang, Ziqian Wang
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Sparsity and incoherence in compressive sampling [PDF]
Inverse Problems, 2007 We consider the problem of reconstructing a sparse signal $x^0\in\R^n$ from a limited number of linear measurements. Given $m$ randomly selected samples of $U x^0$, where $U$ is an orthonormal matrix, we show that $\ell_1$ minimization recovers $x^0$ exactly when the number of measurements exceeds \[ m\geq \mathrm{Const}\cdot ^2(U)\cdot S\cdot\log n, \
Candès, Emmanuel, Romberg, Justin
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Compressive Sampling for Array Cameras [PDF]
SIAM Journal on Imaging Sciences, 2021 While design of high-performance lenses and image sensors has long been the focus of camera development, the size, weight, and power of image data processing components are currently the primary ba...
Xuefei Yan +8 more
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Sample Compression Schemes for VC Classes [PDF]
Journal of the ACM, 2016 Sample compression schemes were defined by Littlestone and Warmuth (1986) as an abstraction of the structure underlying many learning algorithms. Roughly speaking, a sample compression scheme of size k means that given an arbitrary list of labeled examples, one can retain only k of them in a ...
Moran, S., Yehudayoff, A.
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Compressed sampling strategies for tomography
Journal of the Optical Society of America A, 2014 We investigate new sampling strategies for projection tomography, enabling one to employ fewer measurements than expected from classical sampling theory without significant loss of information. Inspired by compressed sensing, our approach is based on the understanding that many real objects are compressible in some known representation, implying that ...
Kaganovsky, Yan +8 more
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