Deriving filter parameters using dual-images for image de-noising

Lingyu Wang, Graham Leedham, Siu Yeung Cho

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a novel technique to derive the filter parameters for removing signal dependent noise (SDN) in the image. In order to remove SDN, many de-noising algorithms rely on a priori knowledge of noise parameters, especially the variance σn2, and the gamma value γ of the specific imaging technique. This paper proposes a technique to automatically derive the signal variance σf2 and use this parameter to construct the Local Linear Minimum Mean Square Error (LLMMSE) filter without the need to know the values of σn2 and γ. Two image instances of the same noisy scene are used to calculate the signal variance which is then used to construct the LLMMSE filter. Experiments with both the "Lena" image and real-life far-infrared (FIR) vein pattern images showed that the proposed technique can predict the signal variance consistently, and the constructed LLMMSE filter performs well in removing the signal dependent noise.

Original languageEnglish
Title of host publication2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages212-215
Number of pages4
ISBN (Print)9781424414475
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007 - Xiamen, China
Duration: 28 Nov 20071 Dec 2007

Publication series

Name2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007 - Proceedings

Conference

Conference2007 International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2007
Country/TerritoryChina
CityXiamen
Period28/11/071/12/07

Keywords

  • Noise removal
  • Parameter estimation
  • Signal dependent noise

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Software
  • Electrical and Electronic Engineering
  • General Mathematics

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