Zobrazeno 1 - 10
of 303
pro vyhledávání: '"Zhang Cishen"'
Publikováno v:
MATEC Web of Conferences, Vol 125, p 05006 (2017)
Stochastic computing (SC) is a computational technique with computational operations governed by probability instead of arithmetic rules. It recently found promising applications in digital and image processing areas and attracted attentions of resea
Externí odkaz:
https://doaj.org/article/d8e05d12d8544cd3888ec18f701f40c9
The quality of images captured in outdoor environments can be affected by poor weather conditions such as fog, dust, and atmospheric scattering of other particles. This problem can bring extra challenges to high-level computer vision tasks like image
Externí odkaz:
http://arxiv.org/abs/1904.08573
This paper presents a novel Block Iterative Bayesian Algorithm (Block-IBA) for reconstructing block-sparse signals with unknown block structures. Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of th
Externí odkaz:
http://arxiv.org/abs/1412.2316
Autor:
Zhang, Cishen, Baqee, Ifat-Al
In parallel magnetic resonance imaging (pMRI), to find a joint solution for the image and coil sensitivity functions is a nonlinear and nonconvex problem. A class of algorithms reconstruct sensitivity encoded images of the coils first followed by the
Externí odkaz:
http://arxiv.org/abs/1408.0622
Direction of arrival (DOA) estimation in array processing using uniform/sparse linear arrays is concerned in this paper. While sparse methods via approximate parameter discretization have been popular in the past decade, the discretization may cause
Externí odkaz:
http://arxiv.org/abs/1312.7695
Autor:
Zhang, Cishen, Baqee, Ifat Al
In parallel magnetic resonance imaging (pMRI) reconstruction without using estimation of coil sensitivity functions, one group of algorithms reconstruct sensitivity encoded images of the coils first followed by the magnitude only image reconstruction
Externí odkaz:
http://arxiv.org/abs/1311.2366
MR image sparsity/compressibility has been widely exploited for imaging acceleration with the development of compressed sensing. A sparsity-based approach to rigid-body motion correction is presented for the first time in this paper. A motion is soug
Externí odkaz:
http://arxiv.org/abs/1302.0077
Phase retrieval refers to a classical nonconvex problem of recovering a signal from its Fourier magnitude measurements. Inspired by the compressed sensing technique, signal sparsity is exploited in recent studies of phase retrieval to reduce the requ
Externí odkaz:
http://arxiv.org/abs/1302.0081
Sparse Bayesian learning (SBL) is a popular approach to sparse signal recovery in compressed sensing (CS). In SBL, the signal sparsity information is exploited by assuming a sparsity-inducing prior for the signal that is then estimated using Bayesian
Externí odkaz:
http://arxiv.org/abs/1208.6464
Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement data is inevitable in practical implementation of CS algorithms. In t
Externí odkaz:
http://arxiv.org/abs/1203.4870