Complexity-regularized denoising of poisson-corrupted data

J. Liu, P. Moulin

Research output: Contribution to conferencePaper

Abstract

In this paper, we apply the complexity-regularization principle to Poisson imaging. We formulate a natural distortion measure in image space, and present a connection between complexity-regularized estimation and rate-distortion theory. For computational tractability, we apply constrained coders such as JPEG or SPIHT to solve the optimization problem approximately. Also, we design a simple predictive coder which lends itself well to our optimization problem.

Original languageEnglish (US)
Pages[d]254-257
StatePublished - Dec 1 2000
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000

Other

OtherInternational Conference on Image Processing (ICIP 2000)
CountryCanada
CityVancouver, BC
Period9/10/009/13/00

ASJC Scopus subject areas

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

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    Liu, J., & Moulin, P. (2000). Complexity-regularized denoising of poisson-corrupted data. [d]254-257. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.