Abstract: This work proposes an extension of a sparse Bayesian learning with dictionary refinement (SBL-DR) algorithm for a super-resolution estimation of ...
Abstract—This work proposes an extension of a sparse. Bayesian learning with dictionary refinement (SBL-DR) algo- rithm for a super-resolution estimation of ...
This work proposes an extension of a sparse Bayesian learning with dictionary refinement (SBL-DR) algorithm for a super-resolution estimation of ...
Numerical simulations show that SBL-DF converges much faster and to more accurate solutions than standard SBL and other dynamical filtering algorithms, ...
Missing: super- | Show results with:super-
Apr 5, 2016 · The proposed method learns dictionary directly from the estimated high-resolution image patches (extracted features), and the dictionary ...
Oct 22, 2024 · In this paper, we develop solutions for sparse tensor signal recovery (SR) and tensor dictionary learning (DL) problems via the sparse ...
Abstract—This paper addresses the problem of learning dic- tionaries for multimodal datasets, i.e. datasets collected from multiple data sources.
Missing: refinement | Show results with:refinement
Most algorithms formulate the dictionary learning as an optimization problem which is solved via a two-stage iterative process, namely, a sparse coding stage ...
Missing: super- | Show results with:super-
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Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms ...
Sparse Bayesian learning with dictionary refinement for super-resolution through time · D. ShutinB. Vexler. Computer Science, Engineering. IEEE International ...