Shift-invariant denoising using wavelet-domain hidden Markov trees

Justin K. Romberg, Hyeokho Choi, Richard G. Baraniuk

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

18 Scopus citations

Abstract

Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training (using the EM algorithm, for example). We use an image structure not yet recognized by the HMT to show that the HMT parameters of real-world, grayscale images have a certain form. This leads to a description of the HMT model with just nine meta-parameters (independent of the size of the image and the number of wavelet scales). We also observe that these nine meta-parameters are similar for many images. This leads to a universal HMT (uHMT) model for grayscale images. Algorithms using the uHMT require no training of any kind. While simple, a series of image estimation/denoising experiments show that the uHMT retains nearly all of the key structures modeled by the full HMT. Based on the uHMT model, we develop a shift-invariant wavelet denoising scheme that outperforms all algorithms in the current literature.

Original languageEnglish (US)
Title of host publicationConference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1277-1281
Number of pages5
ISBN (Electronic)0780357000, 9780780357006
DOIs
StatePublished - 1999
Event33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999 - Pacific Grove, United States
Duration: Oct 24 1999Oct 27 1999

Publication series

NameConference Record of the 33rd Asilomar Conference on Signals, Systems, and Computers
Volume2

Other

Other33rd Asilomar Conference on Signals, Systems, and Computers, ACSSC 1999
CountryUnited States
CityPacific Grove
Period10/24/9910/27/99

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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