Sparse Representation-Based Image Reconstruction

This algorithm implements image reconstruction based on sparse representation principles, primarily applied in image deblurring and super-resolution domains. The core implementation involves constructing an overcomplete dictionary through methods like K-SVD or online dictionary learning, followed by solving sparse coding problems using algorithms such as OMP (Orthogonal Matching Pursuit) or LASSO regression. Through this approach, the system can restore blurred images by representing image patches as linear combinations of a few dictionary atoms, effectively enhancing sharpness and enriching details. Simultaneously, the algorithm can upscale low-resolution images to higher resolutions by learning the mapping between low/high-resolution patch pairs and enforcing sparsity constraints. The reconstruction process typically involves patch extraction, sparse coefficient estimation, and patch aggregation steps with boundary handling. Consequently, this sparse representation-based image reconstruction algorithm demonstrates broad application prospects and can play significant roles in various fields including medical imaging, surveillance enhancement, and digital restoration.