SAR Image Despeckling by Using Nonlocal Sparse Coding Model
Sign Up to like & getrecommendations! Published in 2018 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-017-0704-5
Abstract: Based on the key observation that the coding residuals between the recovered sparse codes of the noisy SAR image and those of the clean SAR image are sparse, we propose a sparse representation-based despeckling algorithm… read more here.
Keywords: image; using nonlocal; sparse; image despeckling ... See more keywords
Novel Circuit Implementation Method for Pulse Signal Finite Rate of Innovation Sparse Sampling Based on an Improved Exponential Reproducing Kernel
Sign Up to like & getrecommendations! Published in 2019 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-019-01076-3
Abstract: Finite rate of innovation sampling is a new signal sparse sampling method based on signal information freedom, which can considerably reduce the amount of sampling data. However, the researches on hardware circuit direct implementation of… read more here.
Keywords: implementation method; rate; sparse sampling; circuit ... See more keywords
Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments
Sign Up to like & getrecommendations! Published in 2019 at "Circuits, Systems, and Signal Processing"
DOI: 10.1007/s00034-019-01111-3
Abstract: AbstractIt is known that the conventional adaptive filtering algorithms can have good performance for non-sparse systems identification, but unsatisfactory performance for sparse systems identification. The normalized least mean absolute third (NLMAT) algorithm which is based… read more here.
Keywords: system; performance; noise environments; sparse ... See more keywords
On logarithmic bounds of maximal sparse operators
Sign Up to like & getrecommendations! Published in 2019 at "Mathematische Zeitschrift"
DOI: 10.1007/s00209-019-02314-9
Abstract: Given sparse collections of measurable sets $$\mathcal {S}_k$$ S k , $$k=1,2,\ldots ,N$$ k = 1 , 2 , … , N , in a general measure space $$(X,\mathfrak {M},\mu )$$ ( X , M… read more here.
Keywords: vert mapsto; sparse; maximal sparse; bounds maximal ... See more keywords
MOEA/D with chain-based random local search for sparse optimization
Sign Up to like & getrecommendations! Published in 2018 at "Soft Computing"
DOI: 10.1007/s00500-018-3460-y
Abstract: The goal in sparse approximation is to find a sparse representation of a system. This can be done by minimizing a data-fitting term and a sparsity term at the same time. This sparse term imposes… read more here.
Keywords: moea; chain based; sparse; sparse optimization ... See more keywords
MAP-based image denoising with structured sparsity and Gaussian scale mixture
Sign Up to like & getrecommendations! Published in 2018 at "Pattern Analysis and Applications"
DOI: 10.1007/s10044-018-0692-5
Abstract: Image denoising is a classical problem in image processing and is known to be closely related to sparse coding. In this work, based on the key observation that the probability density function (PDF) of image… read more here.
Keywords: image; scale mixture; sparse; image denoising ... See more keywords
Sparse large-margin nearest neighbor embedding via greedy dyad functional optimization
Sign Up to like & getrecommendations! Published in 2019 at "Applied Intelligence"
DOI: 10.1007/s10489-019-01472-x
Abstract: We consider the sparse subspace learning problem where the intrinsic subspace is assumed to be low-dimensional and formed by sparse basis vectors. Confined to a few sparse bases, projecting data to the learned subspace essentially… read more here.
Keywords: large margin; sparse; subspace; functional optimization ... See more keywords
Sparse Bayesian learning using TMB (Template Model Builder)
Sign Up to like & getrecommendations! Published in 2024 at "Statistics and Computing"
DOI: 10.1007/s11222-024-10476-8
Abstract: Sparse Bayesian Learning, and more specifically the Relevance Vector Machine (RVM), can be used in supervised learning for both classification and regression problems. Such methods are particularly useful when applied to big data in order… read more here.
Keywords: template model; sparse bayesian; sparse; model builder ... See more keywords
Efficient Uniform Channel Quantization of Sparse CIR for Downlink OFDM Systems
Sign Up to like & getrecommendations! Published in 2017 at "Wireless Personal Communications"
DOI: 10.1007/s11277-017-4201-8
Abstract: Channel state information at the transmitter side is an important issue for wireless communications systems, namely when precoding techniques are employed. Recent works explored random vector quantization (RVQ) as a solution for limited feedback for… read more here.
Keywords: efficient uniform; sparse; quantization; channel quantization ... See more keywords
A-MFST: adaptive multi-flow sparse tracker for real-time tissue tracking under occlusion
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Computer Assisted Radiology and Surgery"
DOI: 10.1007/s11548-025-03414-0
Abstract: Tissue tracking is critical for downstream tasks in robot-assisted surgery. The Sparse Efficient Neural Depth and Deformation (SENDD) model has previously demonstrated accurate and real-time sparse point tracking, but struggled with occlusion handling. This work… read more here.
Keywords: real time; time; sparse; tissue ... See more keywords
Multi-trace nonstationary sparse inversion with structural constraints
Sign Up to like & getrecommendations! Published in 2020 at "Acta Geophysica"
DOI: 10.1007/s11600-020-00430-3
Abstract: The recorded seismic signals are attenuated and spatially correlated due to their propagation through an elastic earth and the sedimentary rule of strata. This attenuation phenomenon is quantified by means of the earth quality factor… read more here.
Keywords: inversion; sparse; multi trace; trace nonstationary ... See more keywords