Footnote:
Part of: Synthesis digital library of engineering and computer science. - Series from website. - Includes bibliographical references (pages 91-104). - Compendex. INSPEC. Google scholar. Google book search. - Title from PDF title page (viewed on May 20, 2014)
System requirements: Adobe Acrobat Reader
Description:
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification