Image Restoration Method Utilizing Sparse Prior

This source code implements an image restoration method based on sparse prior, providing comparative analysis between frequency-domain and spatial-domain restoration approaches. The implementation references the foundational paper "Deconvolution using natural image priors" and includes MATLAB-based algorithms for sparse representation and optimization. Key components feature wavelet transform implementations for sparse coding and iterative shrinkage-thresholding algorithms (ISTA) for solving the optimization problem. The code architecture allows users to experiment with different regularization parameters and dictionary learning techniques to enhance restoration quality. Further exploration can include applications in various image restoration scenarios, parameter tuning methodologies for optimal results, and comparative studies with alternative restoration methods like Wiener filtering or total variation minimization. The implementation also supports integration of recent research advancements and practical case studies to demonstrate real-world performance in areas such as medical imaging and astronomical image processing.