Sparse coding for image/video denoising and superresolution
Learning sparse representations to restore, classify, and sense images and videos Guillermo Sapiro University of Minnesota Supported by NSF, NGA, NIH,
The K–SVD Design of Dictionaries for Redundant and Sparse Representation of Signals Michael Elad The Computer Science Department The Technion – Israel.
Image Denoising with K-SVD Priyam Chatterjee EE 264 – Image Processing & Reconstruction Instructor : Prof. Peyman Milanfar Spring 2007.
Sparse and Redundant Representation Modeling for Image Processing Michael Elad The Computer Science Department The Technion – Israel Institute of technology.
The Quest for a Dictionary. We Need a Dictionary The Sparse-land model assumes that our signal x can be described as emerging from the PDF: Clearly,
Learning sparse representations to restore, classify, and sense images and videos
