Histogram-steered image denoising in the Bayesian framework

Rather than concentrating on modeling the image prior probability whose structure is defined locally, in this paper we incorporate the global information from a histogram into the Bayesian method for image de-noising. The key insight is that the histogram of an underlying image can be approximately...

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Bibliographic Details
Published in2008 9th International Conference on Signal Processing pp. 1178 - 1181
Main Authors Mingsong Dou, Chao Zhang, Daojing Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2008
Subjects
Additive white noise
Bayesian methods
Chaos
Filters
Histograms
Image denoising
Markov random fields
Noise reduction
Smart pixels
Testing
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Summary:Rather than concentrating on modeling the image prior probability whose structure is defined locally, in this paper we incorporate the global information from a histogram into the Bayesian method for image de-noising. The key insight is that the histogram of an underlying image can be approximately recovered from the image with additive noise by a deconvolution operation. We test our algorithm in an image set commonly used for denoising test, and obtain improved results.
ISBN:1424421780
9781424421787
ISSN:2164-5221
DOI:10.1109/ICOSP.2008.4697340