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Blind estimation of white Gaussian noise variance in highly textured images

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationElectronic Imaging
Subtitle of host publicationImage Processing: Algorithms and Systems XVI
PublisherSociety for Imaging Science and Technology
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventIS&T International Symposium on Electronic Imaging -
Duration: 28 Jan 20182 Feb 2018

Publication series

Name
ISSN (Electronic)2470-1173

Conference

ConferenceIS&T International Symposium on Electronic Imaging
Period28/01/182/02/18

Abstract

In the paper, a new method of blind estimation of noise variance in a single highly textured image is proposed. An input image is divided into 8x8 blocks and discrete cosine transform (DCT) is performed for each block. A part of 64 DCT coefficients with lowest energy calculated through all blocks is selected for further analysis. For the DCT coefficients, a robust estimate of noise variance is calculated. Corresponding to the obtained estimate, a part of blocks having very large values of local variance calculated only for the selected DCT coefficients are excluded from the further analysis. These two steps (estimation of noise variance and exclusion of blocks) are iteratively repeated three times. For the verification of the proposed method, a new noise-free test image database TAMPERE17 consisting of many highly textured images is designed. It is shown for this database and different values of noise variance from the set {25, 49, 100, 225}, that the proposed method provides approximately two times lower estimation root mean square error than other methods.

Keywords

  • Blind estimation of noise characteristics, Discrete cosine transform (DCT), Noise free test image database

Publication forum classification

Field of science, Statistics Finland