Optimized Projections for Compressed Sensing

By Michael Elad. IEEE Transactions on Signal Processing, 2007

861 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7e1c9167d6072a1aea1a33082a839068c0521872

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Citations in the corpus

Sparse Representation for Computer Vision and Pattern Recognition (2010, background): “of the sensing matrix for a given dictionary as well as the optimization of the dictionary for a pre-defined sensing environment (see also [31], [60], [69]).”
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy? (2015, background): “While the bounds provided in this case are universally optimal, the introduc tion of a learning phase provides a better reconstruction perfor mance as it adapts the system to the particular data at hand [8], [9], [10].”
Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization (2009, methodology): “Find the largest eigenvalue and corresponding eigenvector of , and .”
Compressed Sensing for Wireless Communications: Useful Tips and Tricks (2015, background): “In this setup, the goal is to design H adapting to dictionary D such that the columns in the combined equivalent dictionary E = HD has good geometric properties [31]–[33].”
Classification and clustering via dictionary learning with structured incoherence and shared features (2010, background): “, [4, 6, 7, 29], where it was shown that both the speed and accuracy of sparse coding techniques such as soft-thresholding and orthogonal matching pursuit depend on the incoherence between the dictionary atoms.”
Convolutional Neural Networks Analyzed via Convolutional Sparse Coding (2016, background): “In addition, from a practical point of view, it was proven beneficial penalizing over this quantity in compressed sensing applications (Elad, 2007).”
Channel Estimation via Orthogonal Matching Pursuit for Hybrid MIMO Systems in Millimeter Wave Communications (2016, methodology): “In [22], and [33]–[35], it has been observed that a carefully designed sensing matrix, which minimizes the total coherence of the equivalent dictionary, can improve the CS performance, as compared with randomized sensing matrices which are popular in CS and have been used for the CS-based channel es”