Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing
Journal of Scientific Computing
Multiridge detection and time-frequency reconstruction
IEEE Transactions on Signal Processing
Adaptive Signal Decomposition Based on Local Narrow Band Signals
IEEE Transactions on Signal Processing - Part I
One or Two Frequencies? The Empirical Mode Decomposition Answers
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Image decomposition via the combination of sparse representations and a variational approach
IEEE Transactions on Image Processing
Morphological Component Analysis: An Adaptive Thresholding Strategy
IEEE Transactions on Image Processing
An optimization based empirical mode decomposition scheme
Journal of Computational and Applied Mathematics
| Hi-index | 35.68 |
The operator-based signal separation approach uses an adaptive operator to separate a signal into additive subcomponents. The approach can be formulated as an optimization problem whose optimal solution can be derived analytically. However, the following issues must still be resolved: estimating the robustness of the operator's parameters and the Lagrangian multipliers, and determining how much of the information in the null space of the operator should be retained in the residual signal. To address these problems, we propose a novel optimization formula for operator-based signal separation and show that the parameters of the problem can be estimated adaptively. We demonstrate the effectiveness of the proposed method by processing several signals, including real-life signals.