Entropic proximal mappings with applications to nonlinear programming
Mathematics of Operations Research
Proximal Thresholding Algorithm for Minimization over Orthonormal Bases
SIAM Journal on Optimization
Total variation minimization and a class of binary MRF models
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Block-Based Compressed Sensing of Images and Video
Foundations and Trends in Signal Processing
An iterative algorithm for signal reconstruction from bispectrum
IEEE Transactions on Signal Processing
Signal recovery from wavelet transform maxima
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Hi-index | 0.00 |
In most compressive sensing problems, @?"1 norm is used during the signal reconstruction process. In this article, a modified version of the entropy functional is proposed to approximate the @?"1 norm. The proposed modified version of the entropy functional is continuous, differentiable and convex. Therefore, it is possible to construct globally convergent iterative algorithms using Bregman@?s row-action method for compressive sensing applications. Simulation examples with both 1D signals and images are presented.