Online dictionary learning algorithm with periodic updates and its application to image denoising

Ender M. Eksioglu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

We introduce a coefficient update procedure into existing batch and online dictionary learning algorithms. We first propose an algorithm which is a coefficient updated version of the Method of Optimal Directions (MOD) dictionary learning algorithm (DLA). The MOD algorithm with coefficient updates presents a computationally expensive dictionary learning iteration with high convergence rate. Secondly, we present a periodically coefficient updated version of the online Recursive Least Squares (RLS)-DLA, where the data is used sequentially to gradually improve the learned dictionary. The developed algorithm provides a periodical update improvement over the RLS-DLA, and we call it as the Periodically Updated RLS Estimate (PURE) algorithm for dictionary learning. The performance of the proposed DLAs in synthetic dictionary learning and image denoising settings demonstrates that the coefficient update procedure improves the dictionary learning ability.

Original languageEnglish
Pages (from-to)3682-3690
Number of pages9
JournalExpert Systems with Applications
Volume41
Issue number8
DOIs
Publication statusPublished - 15 Jun 2014

Keywords

  • Dictionary learning
  • Image denoising
  • Online learning
  • Sparse representation

Fingerprint

Dive into the research topics of 'Online dictionary learning algorithm with periodic updates and its application to image denoising'. Together they form a unique fingerprint.

Cite this