SRRbased
SRRbased is a term used to describe methods and systems built around the principles of sparse representation and regularization. It encompasses algorithms and tools designed to reconstruct data, images, or signals from incomplete or noisy measurements by expressing the data as a sparse combination of dictionary elements and imposing penalty terms to promote parsimony and robustness.
The term originated in the fields of signal processing and machine learning and is used to categorize
Core approach: model the observation y as y ≈ Dα, where D is a dictionary and α is a
Applications span image and audio denoising, inpainting, super-resolution, compressed sensing, and medical or remote sensing imaging.