Shift invariant restoration - An overcomplete maxent MAP framework

P. Ishwar, P. Moulin

Research output: Contribution to conferencePaperpeer-review


Translation-invariant denoising was introduced by Coifman and Donoho to overcome Gibbs-type phenomena produced by transform-domain shrinkage estimators in the vicinity of signal discontinuities. Shrinkage estimators are in general not shift-invariant. Shift-invariant denoising consists of a simple averaging of the shrinkage estimates over a family of cyclic spatial-shifts of the image. Shift-invariant denoising is denoising in an overcomplete basis, and work in this area has been devoted towards finding a best basis in the overcomplete family. This paper presents a Maximum A Posteriori (MAP) framework for shift-invariant restoration of images using the maximum-entropy prior consistent with moment constraints on the transform coefficients in different subbands. The simple averaging of estimates in the classical shift-invariant denoising can then be shown to be a certain limiting case within this framework.

Original languageEnglish (US)
StatePublished - 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000


OtherInternational Conference on Image Processing (ICIP 2000)
CityVancouver, BC

ASJC Scopus subject areas

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


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