Explore MATLAB source code curated for "稀疏表示" with clean implementations, documentation, and examples.
This K-SVD algorithm implementation enables sparse data representation through dictionary training, featuring optimized atom updates and sparse coding using orthogonal matching pursuit (OMP).
Implementation of the KSVD dictionary learning algorithm for sparse representation, capable of generating redundant dictionaries with detailed internal code comments and algorithm explanations
K-SVD Algorithm for Sparse Representation - Published in 2006 with MATLAB Implementation Details and Technical Analysis
Original implementation code by Jianchao Yang for single image super-resolution using sparse representation. The algorithm works by first partitioning high/low-resolution training images into patches, then training patch pairs into coupled dictionaries. Testing images are mapped to the low-resolution dictionary to obtain sparse coefficients, which are then multiplied with the high-resolution dictionary to reconstruct the final image. This implementation provides excellent reference material for students studying super-resolution algorithms and demonstrates practical dictionary learning applications.
This is a sparse representation-based image reconstruction algorithm designed for image deblurring and super-resolution applications, featuring practical implementation with code examples demonstrating dictionary learning and sparse coding techniques.
Implementation of Direction of Arrival (DOA) Estimation Algorithm with Sparse Representation Theory in Coprime Array Signal Processing
Sparse Representation, Dictionary Learning, KSVD Algorithm with MATLAB Implementation and Code Examples
Sparse Representation, Convex Optimization, and Low-Rank Representation for Clustering Analysis
KSVD is a dictionary learning algorithm for sparse representation, where the name derives from K iterations of the algorithm, with each iteration utilizing SVD decomposition to update the dictionary atoms.
Improving Signal Reconstruction Accuracy in Block Compressed Sensing via Directional-Enhanced Sparse Representation Methods with Implementation Approaches