Fundamental equivalences between set-Theoretic & maximum-entropy methods in multiple-domain image restoration

Prakash Ishwar, Pierre Moulin

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


Several powerful, but heuristic techniques in the image denoising literature have used overcomplete image representations. A general framework for incorporating information from multiple representations based on fundamental statistical estimation principles was presented in Ishwar and Moulin (1999) where, information about image attributes from multiple wavelet transforms was incorporated as moment constraints on the underlying image prior. In this paper we explore the fundamental equivalence between the stochastic setting of multiple-domain restoration in Ishwar and Moulin and its deterministic set-Theoretic counterpart. The main technical tool is the Lagrange multiplier theory of constrained optimization. The insights gained by this analysis allow us to derive a state-of-The-Art denoising algorithm.

Original languageEnglish (US)
Title of host publicationSignal Processing Theory and Methods I
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)0780362934
StatePublished - 2000
Event25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000 - Istanbul, Turkey
Duration: Jun 5 2000Jun 9 2000

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other25th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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