Source Code for Image Sparse Representation

This article provides source code implementation for image sparse representation, developed entirely in MATLAB with guaranteed execution stability. The code includes fundamental sparse coding algorithms such as Orthogonal Matching Pursuit (OMP) or Basis Pursuit, enabling efficient image decomposition through dictionary learning techniques. Beyond basic functionality, we offer comprehensive guidance on parameter optimization strategies—including sparsity constraint adjustment and dictionary atom selection—to enhance reconstruction quality for diverse image formats (e.g., grayscale, RGB, medical imaging). Key modular functions cover feature extraction, dictionary initialization, and residual minimization processes. Technical support is available for custom modifications, including handling specialized image preprocessing requirements and performance benchmarking protocols. We anticipate these resources will significantly streamline your sparse representation research and applications.