Results 21 to 30 of about 5,741 (199)
Parameter Determination in Basis Pursuit Denoising via Steins Unbiased Risk Estimate for Underwater Acoustic Communication With Orthogonal Signal Division MultiplexingThis paper proposes a novel method for determining parameters in underwater acoustic (UWA) communication using orthogonal signal division multiplexing (OSDM) combined with basis pursuit denoising (BPDN).
Ryoichi Ishijima +4 more
semanticscholar +3 more sources
European Signal Processing ConferenceSparse Bayesian Learning (SBL) is a widely-used framework for sparse signal reconstruction, yet its standard formulation optimizes model evidence rather than directly minimizing reconstruction error.
Fangqing Xiao, Dirk T. M. Slock
semanticscholar +3 more sources
Monte Carlo SURE-based parameter selection for parallel magnetic resonance imaging reconstruction. [PDF]
Magn Reson Med, 2014 Purpose Regularizing parallel magnetic resonance imaging (MRI) reconstruction significantly improves image quality but requires tuning parameter selection.
Weller DS +3 more
europepmc +2 more sources
Deep Learning Meets Adaptive Filtering: A Stein’s Unbiased Risk Estimator Approach
2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2023 This paper revisits two prominent adaptive filtering algorithms, namely recursive least squares (RLS) and equivariant adaptive source separation (EASI), through the lens of algorithm unrolling. Building upon the unrolling methodology, we introduce novel task-based deep learning frameworks, denoted as Deep RLS and Deep EASI.
Esmaeilbeig, Zahra, Soltanalian, Mojtaba
openaire +2 more sources
Unbiased risk estimation in the normal means problem via coupled bootstrap techniques [PDF]
Electronic Journal of Statistics, 2021 We develop a new approach for estimating the risk of an arbitrary estimator of the mean vector in the classical normal means problem. The key idea is to generate two auxiliary data vectors, by adding carefully constructed normal noise vectors to the ...
Natalia L. Oliveira +2 more
semanticscholar +1 more source
Interferometric SAR Phase Filtering With SURE-Based Non-Local Method
IEEE Access, 2020 As the phase of the interferometric synthetic aperture radar (InSAR) contains abundant information for many earth observation activities, the interferometric phase denoising is an important step before InSAR processing and application because of its ...
Rui Guo +3 more
doaj +1 more source
Empirical Estimation for Sparse Double-Heteroscedastic Hierarchical Normal Models
Journal of Statistical Theory and Applications (JSTA), 2020 The available heteroscedastic hierarchical models perform well for a wide range of real-world data, but for the data sets which exhibit heteroscedasticity mainly due to the lack of constant means rather than unequal variances, the existing models tend to
Vida Shantia, S. K. Ghoreishi
doaj +1 more source
Parameter Selection Criteria for Tomo-SAR Focusing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direction-of-arrival estimation theory.
Gustavo Daniel Martin-del-Campo-Becerra +3 more
doaj +1 more source
Remote Sensing, 2023 Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising
Changlun Gao +7 more
doaj +1 more source
IEEE Open Journal of Signal Processing, 2020 The Approximate Message Passing (AMP) algorithm efficiently reconstructs signals which have been sampled with large i.i.d. sub-Gaussian sensing matrices.
Charles Millard +3 more
doaj +1 more source