Multiscale Sparse Image Representation with Learned Dictionaries (PREPRINT)

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 [1], 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 [2, 3]. 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA478585

Entities

People

  • Guillermo Sapiro
  • Julien Mairal
  • Michael Elad

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computations
  • Computer Programming
  • Computer Science
  • Dictionaries
  • Equations
  • Estimators
  • Gaussian Noise
  • Image Processing
  • Information Operations
  • Learning
  • Linear Algebra
  • Mathematics
  • Minnesota
  • Models
  • Multiscale Models
  • Noise

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.