Learning to Sense Sparse Signals: Simultaneous Sensing Matrix and Sparsifying Dictionary Optimization

By Julio Martin Duarte-Carvajalino, Guillermo Sapiro. IEEE Transactions on Image Processing, 2009

606 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a8b515f2e5d065ed9e8c25710356014262dc0c6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Francis R. Bach
2. Jean Ponce
3. Ali Mousavi
4. Lawrence Carin
5. Julien Mairal
6. David B. Dunson
7. Richard Baraniuk
8. John William Paisley

In Computational Mechanics

1. Francis R. Bach
2. Julien Mairal
3. Jean Ponce
4. Guillermo Sapiro
5. Lei Zhang
6. Richard Baraniuk
7. Peter Richtárik
8. Ali Mousavi

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Julien Mairal
5. Lei Zhang
6. Andrew Zisserman
7. Trevor Darrell
8. Cordelia Schmid

Citations in the corpus

• Exploiting Structure in Wavelet-Based Bayesian Compressive Sensing (2009, background): “Researchers have also developed techniques that impose prior structure through learning the appropriate basis for sparse representation [28], [29].”
• Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images (2011, background): “Recently, there has been significant interest in sparse image representations, in the context of denoising and interpolation [1], [13], [24]–[26], [27], [28], [31], compressive sensing (CS) [5], [12], and classification [40].”
• 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 invert: Signal recovery via Deep Convolutional Networks (2017, background): “Some work has been pursued on learning data-dependent dictionaries to sparsify signals [7–9], but the redundancy of the resulting approaches degrades recovery performance.”
• Task-Driven Dictionary Learning (2010, methodology): “Duarte-Carvajalino and Sapiro [16] have for instance proposed to learn dictionaries for compressed sensing, and in [8], [9], [10] dictionaries are learned for signal classification.”
• Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems (2021, methodology): “The solution for the unconstrained version of Problem (61) has been investigated in [22], and the method designed therein is extended to solve the constrained Problem (61) in [13].”