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

Fingerprint

Imaging techniques

ASJC Scopus subject areas

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

Cite this

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.

Complexity-regularized denoising of poisson-corrupted data. / Liu, J.; Moulin, P.

2000. [d]254-257 Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.

Research output: Contribution to conferencePaper

Liu, J & Moulin, P 2000, 'Complexity-regularized denoising of poisson-corrupted data', Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada, 9/10/00 - 9/13/00 pp. [d]254-257.
Liu J, Moulin P. Complexity-regularized denoising of poisson-corrupted data. 2000. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.
Liu, J. ; Moulin, P. / Complexity-regularized denoising of poisson-corrupted data. Paper presented at International Conference on Image Processing (ICIP 2000), Vancouver, BC, Canada.
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