Compressive Sensing Theory and Sparse Decomposition Toolbox
Compressive Sensing Theory and Sparse Decomposition Toolbox with detailed user manuals and extensive practical routines for implementation
Image Sparse Decomposition Representation Using MP Algorithm Implementation
Implementation of image sparse decomposition representation using the MP (Matching Pursuit) algorithm, fully debugged and operational. This resource provides reference code with detailed explanations of algorithm workflow, basis function selection, and coefficient calculation for learning purposes.
Sparse Decomposition for Signal and Image Processing
Source code implementation for sparse decomposition in signal and image processing, featuring comprehensive documentation and algorithmic explanations in a WORD document format
Compressed Sensing MATLAB Implementation with FFT and OMP Algorithms
MATLAB code for compressed sensing featuring FFT-based sparse decomposition and Orthogonal Matching Pursuit (OMP) algorithm for signal reconstruction, including implementation details for sparse signal processing
Sparse Decomposition of 2D Images Using Matching Pursuit Algorithm with FFT Acceleration
Implementation of 2D image sparse decomposition based on matching pursuit algorithm using FFT acceleration for efficient feature extraction and representation.
Sparse Decomposition Signal Reconstruction
A sparse decomposition signal reconstruction program utilizing matching pursuit methodology to iteratively match signal components and ultimately reconstruct the original signal through optimized atom selection and combination.
Sparse Decomposition Strictly Following Matching Pursuit Principles
Sparse decomposition strictly adheres to matching pursuit principles to generate new atoms that iteratively match with the signal until only residual components remain, implementing an iterative optimization algorithm for signal approximation.
Sparse Signal Decomposition Using Matching Pursuit
Implementation of MP-based sparse signal decomposition with time-frequency distribution analysis
Detection of Weak Sinusoidal Signals Using MP Algorithm in Sparse Decomposition
Implementation of weak sinusoidal signal detection under strong noise background using Matching Pursuit (MP) algorithm in sparse decomposition framework, thoroughly debugged and verified for correctness with detailed code implementation analysis.
Enhanced Signal Matching Pursuit Sparse Decomposition Code
Improved signal matching pursuit sparse decomposition algorithm based on Gabor time-frequency atoms, demonstrating superior speech signal reconstruction performance with optimized feature extraction and computational efficiency.