| Paper: | TA-L4.3 |
| Session: | Image and Video Restoration and Enhancement I |
| Time: | Tuesday, September 18, 10:30 - 10:50 |
| Presentation: |
Lecture
|
| Title: |
MULTISCALE SPARSE IMAGE REPRESENTATION WITH LEARNED DICTIONARIES |
| Authors: |
Julien Mairal; University of Minnesota | | |
| | Guillermo Sapiro; University of Minnesota | | |
| | Michael Elad; Technion - Israel Institute of Technology | | |
| Abstract: |
This paper introduces a new framework for learning multiscale sparse representations of natural images with overcomplete dictionaries. Our work extends the K-SVD algorithm, which learns sparse single-scale dictionaries for natural images. Recent work has shown that the K-SVD can lead to state-of-the-art image restoration results. We show that these are further improved with a multiscale approach, based on a Quadtree decomposition. Our framework provides an alternative to multiscale pre-defined dictionaries such as wavelets, curvelets, and contourlets, with dictionaries optimized for the data and application instead of pre-modelled ones. |
