Multiple basis wavelet denoising using Besov projections

Hyeokho Choi, Richard Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Wavelet-based image denoising algorithm depends upon the energy compaction property of wavelet transforms. However, for many real-world images, we cannot expect good energy compaction in a single wavelet domain, because most real-world images consist of components of a variety of smoothness. We can relieve this problem by using multiple wavelet bases to match different characteristics of images. In this paper, we propose a novel image denoising algorithm that uses multiple wavelet bases. By establishing a new relationship between the deterministic Besov space theory and the wavelet-domain statistical models, we generalize the Besov theory for finite sampled data. After defining convex sets in Besov spaces that contain the true image, we obtain an estimate of the true image by the method of projection onto convex sets. The algorithm outperforms existing multiple wavelet basis denoising algorithms; in particular, it shows excellent performance at low signal-to-noise ratios.

Original languageEnglish
Title of host publicationIEEE International Conference on Image Processing
Place of PublicationLos Alamitos, CA, United States
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages595-599
Number of pages5
Volume1
StatePublished - Dec 1 1999
EventInternational Conference on Image Processing (ICIP'99) - Kobe, Jpn
Duration: Oct 24 1999Oct 28 1999

Other

OtherInternational Conference on Image Processing (ICIP'99)
CityKobe, Jpn
Period10/24/9910/28/99

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Multiple basis wavelet denoising using Besov projections'. Together they form a unique fingerprint.

Cite this