An efficient algorithm for overcomplete sparsifying transform learning with signal denoising

DOI10.1155/2016/6329740zbMath1400.94051OpenAlexW2506997306WikidataQ59140867 ScholiaQ59140867MaRDI QIDQ1793434

Zhihui Zhu, Beiping Hou, Gang Li, Aihua Yu

Publication date: 12 October 2018

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2016/6329740

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Cites Work

• On the identifiability of overcomplete dictionaries via the minimisation principle underlying K-SVD
• Data-driven tight frame construction and image denoising
• Constructing Packings in Grassmannian Manifolds via Alternating Projection
• Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
• Greed is Good: Algorithmic Results for Sparse Approximation
• Designing structured tight frames via an alternating projection method
• Sparse and Redundant Representations
• Parametric Dictionary Design for Sparse Coding
• Dictionary Optimization for Block-Sparse Representations
• Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model
• Learning Sparsifying Transforms
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Quicksort asymptotics
• Subspace Pursuit for Compressive Sensing Signal Reconstruction
• Dictionary Identification—Sparse Matrix-Factorization via $\ell_1$-Minimization
• Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ ¹ minimization
• Compressed sensing

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