Edge-optimized À-trous wavelets for local contrast enhancement with robust denoising

  • Hanika J
  • Dammertz H
  • Lensch H
  • 24

    Readers

    Mendeley users who have this article in their library.
  • 5

    Citations

    Citations of this article.

Abstract

In this paper we extend the edge-avoiding à-trous wavelet transform for local contrast enhancement while avoiding common artifacts such as halos and gradient reversals. We show that this algorithm is a highly efficient and robust tool for image manipulation based on multi-scale decompositions. It can achieve comparable results to previous high-quality methods while being orders of magnitude faster and simpler to implement. Our method is much more robust than previously known fast methods by avoiding aliasing and ringing which is achieved by introducing a data-adaptive edge weight. Operating on multi-scale, our algorithm can directly include the BayesShrink method for denoising. For moderate noise levels our edge-optimized technique consistently improves separation of signal and noise.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Johannes Hanika

  • Holger Dammertz

  • Hendrik Lensch

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free