Enhanced Dictionary Learning: Multi-Dictionary Updates and Coefficient Reuse

The MATLAB code accompanying the SCI paper "Enhanced Dictionary Learning: Multi-Dictionary Updates and Coefficient Reuse" implements several matching pursuit algorithms (OMP, Batch-OMP, and CoROMP) alongside an enhanced K-SVD dictionary learning algorithm. These foundational source codes for image sparse representation research provide crucial resources for studying signal decomposition techniques and dictionary optimization strategies. The implementations demonstrate atomic selection mechanisms through orthogonal matching pursuit, efficient batch processing for large datasets, and collaborative sparse coding approaches. The improved K-SVD algorithm features optimized dictionary atom updates and coefficient reuse methodologies. Detailed examination of these codes reveals implementation specifics of greedy approximation algorithms and dictionary learning iterations, advancing understanding of sparse representations while providing new directions for computational imaging research.