OMP Algorithm for Sparse Reconstruction of Natural Images with MATLAB Implementation
Graph and Manifold Ranking-Based Image Saliency Detection Algorithm with MATLAB Implementation, Featuring Sparse Reconstruction Using Orthogonal Matching Pursuit
Simulation of 256×256 8-bit Grayscale Lena Image with Compressive Sensing
Simulation performed on a 256×256 8-bit grayscale Lena image using DCT matrix as sparse basis, Gaussian random matrix as measurement matrix, and Orthogonal Matching Pursuit (OMP) algorithm for reconstruction.
Reconstruction Algorithms for Sparse Signals
Reconstruction Algorithms for Sparse Signals: Iterative Soft Thresholding (IST), Orthogonal Matching Pursuit (OMP), Lasso Algorithm, Stagewise Orthogonal Matching Pursuit (StOMP), Two-Step Iterative Shrinkage/Thresholding (TwIST)
Implementation of Orthogonal Matching Pursuit (OMP) Algorithm
A straightforward implementation of the Orthogonal Matching Pursuit (OMP) algorithm that aligns with the methodology described in "Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit." This program helps beginners in compressed sensing (CS) quickly grasp OMP fundamentals through practical code examples, featuring step-by-step residual calculations and atom selection processes.
Compressive Sensing Source Code Implementation with Wavelet Sparsification and OMP Reconstruction
High-value compressive sensing source code featuring wavelet basis for signal sparsification and Orthogonal Matching Pursuit (OMP) algorithm for reconstruction. This implementation demonstrates excellent performance with practical applications in signal processing, making it particularly valuable for researchers entering the field of compressive sensing. The code provides clear insights into sparse signal representation and reconstruction algorithms.
Denoising One-Dimensional Noisy Signals Using OMP Algorithm
Implementation of OMP algorithm for one-dimensional signal denoising, referencing M.Elad's MATLAB package for clear understanding and straightforward implementation approach with comprehensive code structure
Compressive Sensing: Wavelet-Based Sparsification with OMP Algorithm for Reconstruction and Recovery
Compressive sensing is an emerging and critically important discipline. This resource presents the most classic and straightforward framework from Hong Kong University's Sha Wei, implementing wavelet-based sparsification followed by Orthogonal Matching Pursuit (OMP) algorithm for signal reconstruction and recovery.
OMP Algorithm for Compressed Sensing: Signal Recovery via Orthogonal Matching Pursuit
Implementation of 1-D signal compressed sensing using Orthogonal Matching Pursuit (OMP) algorithm, where the number of measurements M>=K*log(N/K) with K representing sparsity and N being signal length, enabling near-perfect reconstruction. The algorithm implementation includes greedy iterative selection of atoms from the sensing matrix, residual updating, and least-squares solution for coefficient estimation. Developed by Wei Sha from the University of Hong Kong's Department of Electrical Engineering (Email: wsha@eee.hku.hk). Reference: Joel A. Tropp and Anna C. Gilbert's seminal paper on signal recovery from random measurements.
Introduction to Compressed Sensing with Corresponding OMP Algorithm Implementation
This resource presents Dr. Wei Sha's (University of Hong Kong) foundational work on compressed sensing theory and the Orthogonal Matching Pursuit algorithm, featuring practical implementation insights.
Sparse Decomposition-Based DOA Estimation Algorithm
This algorithm proposes an innovative DOA estimation method based on Orthogonal Matching Pursuit (OMP) framework, utilizing the uncorrelated nature between array manifold vectors for matching pursuit selection to identify the manifold closest to actual source positions.